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EUROPE Europa League - Qualification football predictions and soccer tips. Here are all the Europa League football predictions for today, tomorrow and weekend from our tipsters and experts. All league soccer tips include betting tips from various markets which include 1X2, Over/Under, Both teams to Score, Home and Away and many more. All Europa League soccer tips include Country, League, Time of match, predictions and tipster performance. Previously called the UEFA Cup, the competition has been known as the UEFA Europa League since the 2009–10 season, following a change in format. For UEFA footballing records purposes, the UEFA Cup and UEFA Europa League are considered the same competition, with the change of name being simply a rebranding. There are No EUROPE Europa League - Qualification football predictions and soccer tips. Free Predictions. Country League Time Games Outcome Brazil Serie A Feb 4 12:30AM Corinthians vs Ceara Home Win Either Half (Yes) India I-League Feb 4 10:30AM Real Kashmir vs Indian Arrows 1 India I-League Feb 4 10:30AM Real Kashmir vs Indian Arrows Home Win Either Half (Yes) Greece Greek Cup Feb 4 1:00PM OFI Crete vs Volos 1X&Under 4.5 Uruguay Primera Division - Clausura Feb 4 1:00PM River Plate vs Danubio 1X&Under 4.5 World Club Friendly Feb 4 1:00PM Molde (Nor) vs Brann (Nor) Over 2.5 World Club Friendly Feb 4 1:30PM Kolding IF (Den) vs Fredericia (Den) Over 2.5 World Club Friendly Feb 4 1:30PM Kolding IF (Den) vs Fredericia (Den) 2 United arab emirates UAE League Feb 4 1:30PM Al Nasr vs Khorfakkan 1 World Club Friendly Feb 4 1:30PM Kolding IF (Den) vs Fredericia (Den) Over 0.5 HT. The UEFA Europa League is an annual football club competition organised by UEFA since 1971 for eligible European football clubs. Clubs qualify for the competition based on their performance in their national leagues and cup competitions. It is the second-tier competition of European club football, ranking below the UEFA Champions League. In 1999, the UEFA Cup Winners' Cup was abolished and merged with the UEFA Cup. For the 2004–05 competition a group stage was added prior to the knockout phase. The 2009 re-branding included a merge with the UEFA Intertoto Cup, producing an enlarged competition format, with an expanded group stage and changed qualifying criteria. The winner of the UEFA Europa League qualifies for the UEFA Super Cup, and since the 2014–15 season the winner of the UEFA Europa League also qualifies for the next edition of the UEFA Champions League. The winner enters at the group stage. In 2009–10 season, the competition was rebranded as the UEFA Europa League in a bid to increase the competition's profile. An extra 8 teams now qualify for the group stage consisting of 12 groups with four teams each (in a double round robin), with top two teams place in each group progressing. The competition then progresses in much the same way as the previous format, with four rounds of two-legged knockout rounds and a one-off final held at a neutral ground that meets UEFA's Category Four stadium criteria. The final is played in May, on the Wednesday ten days before the Champions League final. Apart from the teams mentioned, an additional 15 teams eliminated in the Champions League third qualifying round are transferred to the Europa League play-off round, and the 10 losing teams in the Champions League play-off round are transferred to the Europa League group stage. The 12 winners and the 12 runners-up in the group stage advanced to the knock out round, together with eight third-placed teams from the Champions League group stage.
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Bilbao – Huesca = OVER 1.5 ODD: 1.50 FT 2:0. 20:00 Oeste – Vitoria = X2 ODD: 1.30 FT 2:1. 17:00 Karmiotissa – Omonia = 2 ODD: 1.50 FT 1:5. 18:00 Ostrava – Sigma Olomouc = OVER 1.5 ODD: 1.40 FT 1:1. 19:00 Helsingor – Viborg = 2 ODD: 1.75 FT 0:1.
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THIS IS OUR REAL ARCHIVES FROM OUR SOLD FIXED MATCHES: DATE MATCH TIP ODDS RESULT 30.01.2021 Oldham – Salford City 2/1 31.00 0:1/2:1 WON 30.01.2021 Kilmarnock – St. Johnstone 1/2 26.00 2:0/2:3 WON Total odds: 806.00 WON 16.01.2021 Wolves – West Bromwich 1/2 51.00 2:1/2:3 WON 16.01.2021 Chorley – Farsley 2/1 21.00 1:2/3:2 WON Total odds: 1,071.00 WON 09.01.2021 Alloa – Cove Rangers 1/2 24.00 2:1/2:3 WON 09.01.2021 Gloucester City – Kidderminster 2/1 26.00 1:2/3:2 WON Total odds: 624.00 WON 02.01.2021 Huddersfield – Reading 1/2 34.00 1:0/1:2 WON 02.01.2021 Glenavon FC – Linfield 1/2 21.00 1:0/1:2 WON Total odds: 714.00 WON 19.12.2020 Cove Rangers – Clyde 1/2 39.00 2:1/2:3 WON 19.12.2020 Walsall – Port Vale 2/1 24.00 0:2/4:3 WON Total odds: 936.00 WON 12.12.2020 Leyton Orient – Newport County 2/1 29.00 0:1/2:1 WON 12.12.2020 Crewe – Northampton 2/1 21.00 0:1/2:1 WON Total odds: 609.00 WON 28.11.2020 Jonkopings Sodra – Trelleborgs 2/1 22.00 0:1/2:1 WON 28.11.2020 CSA – Ponte Preta 2/1 31.00 0:1/2:1 WON Total odds: 682.00 WON 21.11.2020 Ipswich – Shrewsbury 2/1 29.00 0:1/2:1 WON 21.11.2020 Chateauroux – Sochaux 2/1 31.00 0:1/2:1 WON Total odds: 899.00 WON 07.11.2020 Atletico PR – Fortaleza EC 2/1 29.00 0:1/2:1 WON 07.11.2020 VVV-Venlo – Heracles Almelo 2/1 34.00 0:1/3:2 WON Total odds: 1,326.00 WON 17.10.2020 Dalum IF – Middelfart 2/1 39.00 2:3/4:3 WON 17.10.2020 Volos NFC – Giannina PAS 2/1 29.00 0:1/2:1 WON Total odds: 1,131.00 WON 10.10.2020 Jerv – Raufoss 2/1 31.00 1:2/4:2 WON 10.10.2020 Kalisz – Bytovia Bytow 2/1 28.00 0:1/2:1 WON Total odds: 868.00 WON 03.10.2020 Peterborough – Swindon 2/1 26.00 0:1/3:1 WON 03.10.2020 Burton – Portsmouth 1/2 28.00 2:1/2:4 WON Total odds: 728.00 WON 26.09.2020 Bradford – Stevenage 2/1 27.00 0:1/2:1 WON 26.09.2020 Southend – Morecambe 1/2 29.00 1:0/1:2 WON Total odds: 783.00 WON 19.09.2020 Aarau – Wil 1/2 33.00 1:0/1:3 WON 19.09.2020 Rayo Vallecano – Sabadell 2/1 26.00 0:1/2:1 WON Total odds: 858.00 WON 12.09.2020 Cheltenham Town – Morecambe 1/2 36.00 1:0/1:2 WON 12.09.2020 Livingston – Hamilton 1/2 40.00 1:0/1:2 WON Total odds: 1,440.00 WON 05.09.2020 Municipal Grecia – Alajuelense 1/2 28.00 1:0/2:3 WON 05.09.2020 Municipal – Guastatoya 2/1 30.00 0:1/2:1 WON Total odds: 840.00 WON 29.08.2020 Arda – Botev Plovdiv 2/1 28.00 1:2/3:2 WON 29.08.2020 Aswan FC – Pyramids FC 1/2 24.00 1:0/1:2 WON Total odds: 672.00 WON 22.08.2020 Enskede – Hudiksvalls 1/2 28.00 1:0/1:4 WON 22.08.2020 Valerenga – Sandefjord 2/1 22.00 0:1/2:1 WON Total odds: 616.00 WON 15.08.2020 IFK Hassleholm – Asarums 1/2 25.00 2:1/3:4 WON 15.08.2020 Kanazawa – Ehime 2/1 26.00 0:1/3:2 WON Total odds: 650.00 WON 08.08.2020 Zweigen Kanazawa – Ehime 2/1 27.00 0:2/4:3 WON 08.08.2020 Daejeon Citizen – Gyeongnam 1/2 26.00 2:0/2:3 WON Total odds: 560.00 WON 01.08.2020 Napredak – Partizan 1/2 20.00 1:0/1:3 WON 01.08.2020 Incheon United – Gwangju 1/2 28.00 1:0/1:3 WON Total odds: 560.00 WON 18.07.2020 Moss – Eidsvold 1/2 39.00 1:0/1:2 WON 18.07.2020 Bryne – Flekkeroy 2/1 27.00 0:1/3:1 WON Total odds: 1.053.00 WON 11.07.2020 Gornik Leczna – Pogon Siedlce 1/2 32.00 1:0/2:3 WON 11.07.2020 Stomil Olsztyn – Miedz Legnica 1/2 23.00 1:0/1:2 WON Total odds: 736.00 WON 04.07.2020 Setubal – P. 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Why Our Correct Score Tips? Correct Score is a bet type that highlights the exact score of the outcome of a given match in soccer. Most bookmakers present it in many forms with the most being fixed to first half or full time. It usually one of he most crucial bet to predict but our Experts got you covered. Top Tips Today. Table below shows top most correct score tips and predictions for today. Time League Match Pick 02:05 Colombia 02:05 Colombia - Primera A, Apertura, Round 4 Alianza Petrolera vs Rionegro Aguilas Doradas Correct Score HT(1-0) 04:00 Costa Rica 04:00 Costa Rica - Primera Division, Clausura, Round 6 San Carlos vs CS Cartagines Correct Score HT(1-0) 13:00 Turkey 13:00 Turkey - TFF 3. Lig, Grup 1, Round 17 Bucaspor 1928 vs Diyarbekirspor Correct Score FT(2-1) 14:30 Iran 14:30 Iran - Persian Gulf Pro League, Round 14 Tractor vs Persepolis Correct Score FT(0-1) 16:00 Uruguay 16:00 Uruguay - Primera División, Clausura, Round 4 Club Atletico River Plate vs Danubio Correct Score HT(1-0) 18:00 Ghana 18:00 Ghana - Premier League, Round 12 Medeama vs Accra Hearts of Oak Correct Score HT(0-0) 19:15 United Arab Emirates 19:15 United Arab Emirates - UAE Pro League, Round 15 Al-Wahda FC vs Al-Sharjah Correct Score FT(1-2) 19:30 Italy 19:30 Italy - Serie C, Girone B, Round 22 Mantova vs Padova Correct Score HT(0-1) 20:00 Guatemala 20:00 Guatemala - Liga Nacional, Apertura Playoffs, Final Deportivo Guastatoya vs CSD Municipal Correct Score FT(1-1) 20:30 World 20:30 World - Club World Cup, Round 2 Al Duhail vs Al Ahly Correct Score HT(0-0) Its is usually abbreviated as "CS" and its annotation is as follows; CS HT(0-0), CS FT(2-1), CS HT(1-0), CS FT(1-1) etc. Most bookmakers have a huge number of odds placed on this bet type, hence our free tips helps your to increase your chances of winning on this bet. Tomorrow's Tips. All correct score prediction of the matches scheduled to play tomorrow. The correct score matches will be updated soon. Otherwise subscribe to our Expert tips or Premium tips. Best market for correct score tips include Egypt soccer competition, Greece Super League, Turkish Super Lig just to name a few. This is not always guaranteed due to ever changing nature of events. Use our exact score tips to boost your chances of winning. Yesterday's Tips. Outcome of correct score predictions yesterday.
Correct Score Betting Tips. Are you looking for winning Correct Score Betting Tips to help you make a chunky profit from your betting? We have had winners at odds ranging from 40/1 all the way up to 360/1. On an average day the odds of our correct score tips will usually be around 90/1 so a £10 bet would return over £900. Want to See ALL the Correct Score Predictions? Predictions price in other currencies: 520 NGN, 149 KES - We accept card payments and Paypal. Want More Betting Tips? Here are some of the most popular areas of the site that you may also want to check out whilst you are here picking your bets: If you are serious about making a profit from your football betting, then you NEED to take a look at our PRO TIPS section where we give you the exact bets you need to place every day to make a consistent profit from your betting. Are you ready to make a profit from your football betting? What Is Correct Score Betting? With Correct Score betting you are trying to pick the exact final score of a football match. Rather than simply picking a team to win you need to also correctly predict the exact number of goals each team is going to score in the game. This means that you will get much better odds due to the complexity of the bet. As an example, Man City to beat Watford might be available at odds of 1.50 (1/2) - however, Man City to win 3-0 may be at odds of 8.00 (7/1) which offers much better value. How To Pick Correct Score Bets? Correct score bets are very difficult to predict consistently however if you use a methodical approach you can narrow down the number of options. Firstly you need to narrow down whether or not you think both team will score in the match as if you think they will then this will remove all the win-to-nil options like 1-0, 2-0, etc. Once you have made a decision on BTTS it is then time to decide on the number of goals in the match, we tend to look at whether it will be over or under 2.5 goals. Finally we need to combine our goals and BTTS thoughts with who we think will win the game. As an example using the Man City vs Watford game mentioned above - Chance of BTTS = Yes, Chance of Over 2.5 Goals = Yes, Likely Winner = Man City, this leaves us with the follow best predictions 2-1, 3-1, 4-1. You can either use your own judgement at this point or potentially could look at any previous meetings or previous winning margins to narrow the selections further. As Man City normally win by more than one goal at home the 3-1 prediction would be most likely. As you can see it is a fairly long process - which is why having an algorithm do it for you is beneficial. Use our tips above to save time and win more correct score bets. Best Way To Use Correct Score Tips. Using the above correct score tips in singles is an obvious option given that the average odds will likely be around 7.00 (6/1), however, the most profitable way of doing may actually be to build out some multi bets and accumulators. Personally, we like to combine them into Correct Score Doubles, as these are lower in risk and can have odds around 49.00 (48/1) which wins £480 from a £10 bet. Another option we like to employ is to pick five correct score tips and combine them as doubles. This will create a ten line bet, so £1 a line will have a £10 total stake and you only need two out of the five to come in for a decent profit, any more than two winners will deliver a huge profit. If you are really confident you can go for trebles for average odds per line of around 343.00 (342/1)! What is a Correct Score Double? A Correct Score Double is when you select two correct score picks and back them together to create a higher odds bet that needs both correct scores to be correct in order to win. These can be very attractive for punters as the returns can be very high however, the risk is also very high as well so don't invest heavily in these selections. Pro Tips. Sign up today and get access to the best football betting tips available! You must be 18 years old or over to use this site. Please bet responsibly.
Betting Detectives. Your Daily Dose Of Betting Tips. Correct Score Tips. We are chasing some high-odds correct scores today. Correct Score Double for Wednesday, 3rd February: Strasbourg v Brest – 2:0 FT @ 9.00. Metz v Montpellier – 1:1 FT @ 6.00. Correct Score Double for Tuesday, 2nd February: Newcastle v Crystal Palace – 1:1 FT @ 6.50 – LOST. Rizespor v Kasimpasa – 1:1 FT @ 6.50 – WON. Correct Score Double for Monday, 1st February: Real Betis v Osasuna – 2:1 FT @ 8.50 – LOST. Odisha v Jamshedpur – 1:1 FT @ 6.00 – LOST. Betting with 22Bet. WHICH FREE BET ARE YOU GOING TO CLAIM TODAY? 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Correct Score Tips. This is the place for dreamers and those who chase big wins. Obviously, the money or the win is anything but guaranteed, but the fun most certainly is. If from time to time we get to win big and hit our correct score double, the entertainment will be even greater. As you most probably know, correct score is one of the toughest markets to predict. However, the odds are proportionally much higher than those you will find with other markets. Landing a single correct score bet is tough. We are going to chase doubles. That’s how brave we are. Correct Score Predictions – Big Profits. Why did we decide to have this feature on the site after all? On quite a decent number of occasions, our detectives have managed to land sick odds doubles over the years of betting. Since the theme of the page itself is football betting, we just could not skip good old correct scores. Detectives will try and update this section regularly. This means that you can expect daily correct score predictions. There is enough of football action across the globe literally on a daily basis. Since detectives will be analysing most of matches, predicting the most likable scores should not be too big of a challenge . Best Place to Bet on Correct Score. Online bookmakers frequently go out with special promotions connected to correct score bets. We will also do our job on this field and make sure to let you in on where to place our correct score double picks to get the most of them. Normally, it’s the massive price boost for certain scores. If these match our predictions, that’s a win-win situation. Also, you can always check our promotions section and back these “risky” tips using exclusively free bets. Strategy For Correct Score Predictions. There is not a 100% win strategy for this type of market. What we can do is make our chances as high as possible. Backing 1-1 or 0-0 draws is of the formulas that have used to work very well for us so far. The main principle is to check teams’ form and their H2H stats. If the sides have the similar quality and if you believe the game will end out in a draw, backing correct score instead of the outcome can actually be much more profitable. At the end of the day, correct score is a combination of a possibility for a huge profit and fun with minimum investments. It will be very interesting to follow our progress here. Correct Score Double for Monday, 9th November: 42.00 DOUBLE WON! Kiel v Hamburger SV – 1:1 FT @ 7.00 – WON. Girona v Las Palmas – 1:1 FT @ 6.00 – WON. Correct Score Double for Saturday, 28th November: 39.00 DOUBLE WON! Augsburg v Freiburg – 1:1 FT @ 6.50 – WON. Elche v Cadiz – 1:1 FT @ 6.00 – WON.
2 Correct Score Predictions with High Odds,4/2/2021: Win Up to Ksh300,000 per Day. We send only two correct score games every day where each subscriber is guaranteed that one of them will win. We also send a multibet of 4-6 games daily. So when you subscribe to correct score tips it’s like you have paid for the multibet category, correct score and under 3.5 goals category. The chances of winning daily are 99%-100%. If you combine today’s correct score games, the total odds will be above 35, with a stake of Ksh1,000, you’ll win over Ksh35,000. To subscribe for correct score category, you pay Ksh1,999 per month, Ksh900 for two weeks or Ksh600 per week as follows: Go to MPESA Select Lipa Na MPESA Select Buy Goods and Services Enter MPESA till number (705909) Enter amount (Ksh1,999, Ksh900,Ksh600) Enter MPESA pin and send. The predictions will be sent to you daily via SMS. Below is a list of free correct score tips,4/2/2021: Nevsehir Belediye Spor vs Edirnespor(1:1) Padideh Mashhad vs MES Rafsanjan FC(1:1) Teraktor Sazi vs Persepolis FC(1:1) KMC FC vs Namungo FC(1:1)
Correct Score Betting tips with High Odds. Correct score tips are part of the tips we provide here at Venas News. Today we have a total of 2 sure games with high odds. If you feel like earning up to Ksh100,000 per day, these are the type of tips to bet for. One of the games contains 14 odds and another one 7 odds. To subscribe for the correct score betting tips, pay Ksh1,900 per month, Ksh900 for two weeks or Ksh600 per week as follows: Go to MPESA Select Lipa Na MPESA Select Buy Goods and Services Enter MPESA till number (705909) Enter amount (Ksh1,900, Ksh900,Ksh600) Enter MPESA pin and send. We send 2-4 sure correct score matches on daily basis. Below are free correct score tips for trial: QPR vs Stoke City(1:1) Stellenbosch FC vs Cape Town City(1:1) SuperSport Utd vs Kaizer Chiefs(1:1)
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Sure Bets Today. What are the best sure bets and sure bet predictions? Our sure bets listed below help you find the games and bets that have the lowest odds if you are looking for low odds games and selections to put into your banker accas. Sure bets are NOT guaranteed bets or wins. You are currently viewing below the most sure bets and lowest odds for games played today. Sure Bet Predictions Today. Here are the lowest priced sure bets for today's games. Click on any game to view the analysis for this game and click on any bet selection text to view statistics for that given league. What Is A Sure Bet or a Sure Win? Sure bets at WinDrawWin are some of the lowest priced odds and market selections that we have found for today, tomorrow and this weekend. They represent bets that the bookies have priced so low that they think the probability of these events happening is high or very high. Of course there is no such thing as a complete sure bet, because sport is sometimes random and no bet is ever a guaranteed success. We have put these pages together to help WinDrawWin users build their sure bet accumulators by having all the lowest odds games and selections in one place, instead of having to try find these markets and games on the bookmaker websites.
Sure Soccer Betting Tips From Best Tipsters. These are free soccer betting tips and predictions with 70% win guarantee. Want sure odds? you can join our sure VIP or VVIP betting tips from professional betting tipsters. Get ensured every day! Free Daily Betting Tips. SURE WINNING MATCHES IN THE VVIP AND EXPERT TODAY ( SURE WINNING GAMES IN THE VVIP TODAY PAY NOW AND WIN BIG!!) SURE WINS AVAILABLE TODAY IN ALL THE PLATFORMS × 1X2 GG Over/Under. Today 1X2 Predictions. Today GG Predictions. The matches will be updated soon! Check them out in a moment. Thank you! Today Over/Under Predictions. 1X2 GG Over/Under. Yesterday 1X2 Predictions. Yesterday GG Predictions. Yesterday Over/Under Predictions. Sure Bets Today, the expert in Football Betting Tips and predictions. Our predictions cover all the major sporting leagues and tournaments that take place. This includes the English Premier League, Champions League, Europa League, LaLiga, Italy Serie A, Portugal Primeira Liga, Germany Bundesliga, Netherlands Eredivisie, French Ligue 1, to name but a few. Premier League. The most popular league in the world, we cover every match in the English Premier League. With the league’s popularity, you will find a huge range of predictions and tips. Laliga Santander. The league containing stars like Lionel Messi, Eden Hazard and Antoine Griezmann, who wouldn’t want to get involved? Find our detailed LaLiga football predictions. Italy Serie A. A tactician’s dream league with the likes of Juventus, Lazio and Inter Milan. Italian Serie A’s disciplined football matches are the best bet markets for our expert betting tipsters. German Bundesliga. A thrilling heavy-metal football, many of the world’s most exciting teams play their games in the Bundesliga. Every day of the week you will find predictions on upcoming events. Champions League. Sure Bets Today is the home of sure UEFA Champions League betting predictions. We cover everything, from group stage qualifiers all the way through to the final in May. Europa League. Are you looking for an all-in-one betting package that will make continual profits? No soccer cup competition in Europe boasts as many matches as the UEFA Europa League. Our Pricing & Plans. Are you looking for an all-in-one betting package that will make continual profits? Do you dream of making extra money with very little effort and only a small amount of investment? Sure Bets Today specializes in finding the best sure bets for you each day, helping to make your betting life more profitable. VIP Tips. Ksh. 150 / 3-Days. VVIP Tips. Ksh. 350 / 3-Days. Expert Tips. Ksh. 500 / 3-Days. Account Management. Ksh. 650 / 3-Days. History of Winning Tips. Our betting tips are provided regularly by experts in a wide range of sports to help you build your bankroll and have successful tournaments. The pure range and diversity of winning tips posted at surebetstoday is truly amazing. VIP Win History. Best football tips. VVIP Win History. Expert Win History. Frequently Asked Questions. Our main focus is on the football betting tips and predictions which we shall be offering the tips daily. Surebetstoday also offers tennis betting tips, basketball betting tips, ice hockey betting tips and rugby betting tips. We know that lots of football gamblers like to place accumulator bets, especially at the weekend. In order to beat the bookies we would always advise singles, doubles or trebles but of course, everyone likes a shot at glory and so we post more ambitious accas when the fixtures allow it. Our advice is to keep stakes small on these bets but it’s OK to dream big and to try to land a monster accumulator from time-to-time. 1-tip on home team winning, X-tip on tie to end as a draw, 2-tip on visiting team winning, 1X- tip on home team not loosing, X2-tip on visiting team not loosing. It is about betting on how many goals will be scored. Under means that less than 3 goals will be scored during the match(0:0, 1:0, 0:1, 2:0, 0:2 ,1:1), over, on the other hand is a betting on more than two goals will be scored during the match. Example over 3.5 means more than three goals will be scored in the match (4:0, 3:1, 2-2,). G.G means both teams to score during the match.
Daily 2 Sure odds Prediction & Banker bet. DAILY Two Sure 2-3 Odds Prediction & Banker bet. Our today daily free (two) 2 to 3 sure odds prediction and banker bet are 95 - 100% accurate. Our team works with top professional football analyst and punters. Banker games mean games with accuracy ranging from 90 to 100 per cent. Our tipster evaluates all available games then choose from them to form our banker bet. If you have been following us up you will notice that we have been recording high winning rate with our banker bet. Our daily 2 odd games are the best among the best in the world and we have a low record of loss in this section of betting. we will advise you to monitor our games for a week before using our site tips.
Over 1.5 Goals Tips. Over 1.5 Goals bets are a popular pick when people are looking to create accumulators - the trick is to not go over the top and stick to fewer selections rather than trying to get a 10-fold to win! Below you can find our advised Over 1.5 Goals treble for today. Do you want to get more serious with your betting? Consider joining our PRO DATA platform where you can get access to a sortable list of all the best betting data on a daily basis! Our data is used by some of the most well known tipsters on the Internet! What Is Over 1.5 Goals? The over 1.5 goals tips listed above are selections where the algorithm believes the game will have at least two goals in it. With this bet you will lose if the game has zero or one goals and win if there are two or more. Due to the high hit-rate on these types of tips they are becoming a very popular betting choice and they are fairly simple to understand which means they appeal to the masses. How To Pick Over 1.5 Goals Bets? Picking games that are likely to have over 1.5 goals comes down to analysing the average goals per game for the respective team. By doing this you can get a feel for teams that are generally involved in games with goals. It is also a good idea to look at teams 'Failed to Score' & 'Clean Sheet' percentages to remove games where teams are more likely to keep clean sheets. Given that the average goals per game is generally over two, you are more interested in trying to remove selections that are more likely to be under 1.5 goals that you are trying to pick games that will be over 1.5 goals. Best Way To Use Over 1.5 Goals Tips. As the odds of these selections are often too low to cover them in singles, if can often be better to combine these into trebles and four-folds for better value. Given the average odds will likely be around 1.30 per selection, going for a four-fold will likely give you average odds of 2.85. Whilst this is still low it is a high accuracy bet so you can slowly build up your betting fund by placing one of these bets daily using our tips. In short, adopting an over 1.5 goals accumulator strategy using the above selections is the way to go! Pro Tips. Sign up today and get access to the best football betting tips available! You must be 18 years old or over to use this site. Please bet responsibly.
Over 1.5 Goals. Football predictions on teams to score at least 2 goals in a game shouldn’t be a major challenge right? But! It is! It is a very big challenge that has seen even the best-attacking teams come up short in scoring. ACCURATE OVER 1.5 TIPS. COMPETITION MATCH TIP Premier League Tottenham vs Chelsea Over 1.5 Denmark Nordsjaelland vs Brondby Over 1.5 Greece Asteras vs Aris Over 1.5 India North East vs Goa Over 1.5 Portugal SC Farense vs Santa Clara Over 1.5 Portugal Braga vs Portimonense Over 1.5 Saudi Arabia Al Ain vs Ettifaq Over 1.5 Copa Del Rey Betis vs Athletic Bilbao Over 1.5. The over 1.5 goals predictions we offer can help you win bets on a regular basis based on the predictions we make on the day. Take advantage of the over 1.5 goals predictions from a very reliable betting tips site that gives you access to accurate over 1.5 tips. BEST OVER 1.5 SOCCER PREDICTION SITE. The over 1.5 goals predictions or football tip is one of the most popular football betting predictions and it demands that at least two goals must be scored in the game before the final whistle. However, winning is only in view if they are accurate over 1.5 prediction tips. The only downside of this wonderful option where at least two goals must be scored in a game in 90 minutes is that you’ll have to accumulate many games,.as the odds majorly attached to the option is very small when compared to other betting options. We’ve seen games where teams that are supposed to produce an attacking masterclass fall short of the hype and sometimes play 0:0 and this is heartbreaking. This is one of the experiences you’ll get as an over 1.5 goals punter and this is a fact. In this betting option, both teams don’t need to score, all that is required is that a minimum of 2 goals must be scored in a game. This might seem easy to the eye, but it is not the case. It also requires careful analysis and luck has a major part to play. We’ve seen where potentially exciting games end in goalless draws or with just one goal being scored in the game. A scoreline like 2:0, 1:1, and above will see the best over 1.5 goals prediction materializes. However, a scoreline like 0:0 and 1;0 means that the option is busted. Do well to take advantage of the best over 1.5 predictions we offer. This tip is one of the best you can find on any prediction site. Enjoy our reliability in offering accurate over 1.5 tips.
Over/Under 1.5 Goals Statistics. Over/Under 1.5 Goals betting is popular because it is a market that offers a higher degree of certainty and predictability than other markets, albeit with low odds. Across all leagues the average number of games in which there are over 1.5 goals surpasses 70%, and in some leagues over 1.5 goals can be as high as 85% to 90% of all games. What is Over/Under 1.5 Goals betting? If you are wondering how over/under 1.5 goals betting works, it's pretty simple. If you back a game to be over 1.5 goals then there must be 2 or more goals scored in total between the two teams in that match for your bet to win. If you back under 1.5 goals then there must be zero or 1 goal in total between the two teams in that match for your bet to win. Who Are The Best Teams For Over/Under 1.5 Goals Games? Across all of the leagues that WinDrawWin covers, here are the best teams this season for over 1.5 goals games. What Does Over 1.5 Goals Mean? How Do You Calculate Over 1.5 Goals. Over 1.5 Goals is a betting market that relates to the total number of goals scored in a game, that is the sum of the home team and away team goals. For example, if the home team scores one goal and the away team scores one goal, then the total goals in the match is two. Therefore in this example the game would end with over 1.5 goals. Similarly, if the home team scores two goals and the away team does not score, then the total goals in the match is two. Both teams do not need to score for a game to end with over 1.5 goals. Do Both Teams Have To Score For Over 1.5 Goals? No. For a game to end with Over 1.5 Goals, both teams do not necessarily have to score. One team, either the home team or away team could score 2 or more goals and the game would end with over 1.5 goals. Why Is It Over 1.5 Goals? The 0.5 in Under/Over 1.5 goals betting ensures that the betting market is a 2-way market. There can be no tie. Because bets are placed on over or under 1.5 goals then the result for a game must be either over or under (2-way). Where Can I Find Over 1.5 Goals Tips? All our predictions include a correct score prediction in which an over 1.5 goals tip is included. If our score prediction home goals and away goals add up to more than 1.5 then we are tipping over 1.5 goals. Check out our current predictions on our Predictions Today page. What Percentage Of Games End With Over 1.5 Goals? The percentage of games that end with over 1.5 goals varies by league. However, in general across all world leagues approximately 70% to 80% of games have 2 goals or more in total. However, check each of our Under/Over By League pages for the exact average percentage of games by each league. For example, check out our English Premier League Over 1.5 Goals Stats where you will find the average percentage of games in the Premier League for under/over 1.5 goals. How Do You Predict Over 1.5 Goals? Some tips on how to predict over 1.5 goals games. You might want to consider - Given the average percentage of games in a league where there are 2 goals or more is on average 70% to 80%, it might be worth considering what factors make a game likely to be under 1.5 goals. For a game to be under 1.5 goals there are only three scorelines that the game can finish with - 0-0, 1-0 or 0-1. All other scorelines are over 1.5 goals. If you're looking for the best leagues for low scoring games then check out our scoreline stats pages. We have got Best Leagues For 0-0 Results, Best Leagues For 1-0 Results and Best Leagues For 0-1 Results and all other scorelines. The likelihood that both teams might score in a game. It's relatively easier for a game to be over 1.5 goals if both teams score, because otherwise you are relying on one team to score multiple goals, if you are betting on over 1.5 goals How important the game is to either team. If the game might decide promotion, or a playoff spot, or perhaps it is a battle between two teams at the bottom of the table, you might want to consider if these teams might go for lots of goals, or conversely might settle for a low scoring win. In some cases when a game is really important to both teams then these games can be low scoring when teams do not want to set up their formations to attach Team lineups and formations. Sometimes a weaker team might set up their formation to try neutralise an opponent. For example playing just one striker and 5 in midfield. This can sometimes mean that what might seem like an obvious high scoring game becomes a stalemate, and ultimately low scoring. Past form. You might want to look back at each teams' past form at home and away. Do the teams playing score lots of goals? On how many occasions has each team scored 2, 3, or more at home or away this season? Even good form teams can sometimes score very few goals.
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What is correct score flex. This is based on having the same raw to scaled conversion table, but scored as though there was only one LR section (one half of the usual amount). No one outside of LSAC knows how the scoring will actually be done, so is just for illustrative purposes. PrepTest: LR Incorrect: Two LR sections LG Incorrect: RC Incorrect: It's frustrating and disorienting not to know how the Flex test will be scored. The LSAT is stressful enough without worrying about a new format. But, the truth is, you've already been given the best converter in existence from the LSAC itself: the regular 4 section PrepTest. Take 4 section PTs. That will be the best predictor of how you will do on a 3 section Flex test. On test day, frame the loss of 1 LR section to yourself as a treat: 1 fewer stress inducing nerve-racking task to do. We've debated creating a "Flex score converter" and a "Flex PT" and we've been hesitant to do so because of how speculative it would inherently be. The truth is that only LSAC can create a "Flex score converter" or a "Flex PT." LSAC has not given any significant details on how they will score LSAT Flex. Anything we try to do on that front will necessarily be guesswork and misleading. Having said that, we made this "Flex Score Estimator" based on requests by students to see what their score would be if LR, RC, and LG were weighted the same. Feel free to play around with it and don’t take it seriously! Leave a Reply Cancel. You must be logged in to post a comment. You can get a free account here.
Choosing the Right Shaft Flex in Your Golf Clubs. Understanding Golf Club Shaft Flex Can Help Improve Your Scores. If you want to avoid giving your golf game the shaft, you need to understand the effect that golf club shaft flex has on your game. "Flex" refers to the ability of a golf shaft to bend as forces are applied to it during the golf swing. Those forces are generated by the type of swing that you have: fast or slow, smooth or jerky. There are five generally used ratings for shaft flex: Extra Stiff, Stiff, Regular, Senior and Ladies, usually denoted by the letters X, S, R, A and L ("A" is used for Senior because this flex was originally called "amateur"). Having a flex that doesn't match the needs of your swing will result in the clubface being misaligned at impact, causing your shots to go off-target. What Shaft Flex Impacts. Shaft flex impacts, either directly or indirectly, the accuracy, trajectory and distance of your shot. Three pretty important things in golf. As the shaft flexes throughout the swing, the position of the clubhead changes. And the face of the club must be square (perfectly straight, or parallel to the target line) at impact to get the most out of the shot. If you have the wrong flex for your swing, there is less chance that you'll make contact with the ball with a square clubface. Some General Guidelines About Shaft Flex. The degree of flex in your club shafts is something you control. You can choose to buy stiffer shafts, or softer shafts, depending on your needs. But how can you tell what you need? Here are some general guidelines: Take a look at the distance you hit your driver. This is a good, but very general, indicator. If you carry you driver 250 yards or more, go with Stiff; 230-250 yards, Regular; 200-230 yards, Senior; less than 200 yards, Ladies. Only the biggest of the big hitters is going to need Extra Stiff. For most of us, Extra Stiff isn't even in the picture. If you have a very smooth swing, you might benefit from a softer flex even if you swing fast. A swing that gets jerky at the top—when transitioning into the downswing—will probably need a stiffer shaft. If your drives go left, you might benefit from a stiffer flex; if your drives go right, you might benefit from a softer flex. If Your Shaft Flex Is Too Stiff . What effect does a too-stiff shaft have on your golf game? The ball will probably fly lower and shorter for any given loft, compared to a properly fit shaft. The ball may tend to go to the right, or fade side, for right-handed golfers because with a too-stiff shaft the clubface is harder to square (the clubface is more likely to be open at impact, in other words). The shot may feel less solid, more like a mishit even if you make contact on the center of the clubface. If Your Flex Isn't Stiff Enough . The ball might fly higher for any given loft, compared to a properly fit shaft. The ball may tend to go left, or to the draw side, for a right-handed golfer (because with a too-flexible shaft, the clubhead may tend to come into the ball closed). Shots may tend to feel more solid, even when they aren't. Oh, Those Macho Men. Men like to hit golf clubs with Stiff shafts. It's a guy thing. Unfortunately, it's not always the smart thing. No macho man wants to be seen hitting a wimpy little Regular flex club, or, Tiger Woods forbid, a Senior or Ladies flex. But over-swinging is a common problem among male high-handicappers. Choosing a softer flex often has the effect of forcing macho men to slow down their swings. And slowing down the swing often makes those macho men into better golfers. And the fact is, the harm in hitting a shaft that is too flexible is much less than the harm in hitting a shaft that is too stiff. As equipment guru Tom Wishon has said, when unsure about flex, always err on the side of more flex (meaning, a softer shaft). If you can't decide between Regular and Stiff, go with Regular. The Foolproof Way to Choose Shaft Flex. A clubfitting with a golf professional is the recommended way to choose the proper amount of flex in your golf clubs. The pro will take a lot of measurements, watch your swing, measure your swing speed, watch your ball flight and be able to recommend the flex that is right for you. Clubfittings are available at many pro shops and almost all golf schools and from teaching professionals, at courses and full-service driving ranges. Dedicated clubfitters are also becoming more common. If a club-fitting isn't in your future, the next best thing is a demo day. At demo days, you'll be able to hit many different types of clubs with different types of shafts. Or find a good pro shop with swing bays where you can try before you buy. The key, short of a club fitting, is hitting lots of different clubs and watching the effect that changing shaft flex has on your shots. If you find a flex that feels good and produces a good ball flight, there's a good chance that's the right flex for you.
LSAT-Flex Score Conversion Table (How to Predict Your LSAT Flex Score!) If you’re planning on taking the LSAT-Flex at-home exam in 2020 or 2021, you’re probably wondering how you can predict your score knowing that the LSAT-Flex is different than the standard LSAT. Since LSAC announced the at-home LSAT-Flex option in April 2020, the number one question we’ve been getting from students is “How can I predict my LSAT-Flex score?” And this question will keep being important: all LSATs through at least April 2021 will be LSAT-Flexes. Below you’ll find a table that converts projected LSAT-Flex raw scores to standard LSAT raw scores to LSAT percentiles. These are based on the May 2020 administration of the LSAT-Flex and LSAC’s raw to scaled conversion chart. Note that these conversions may change slightly during future administrations. Table of Contents. How We Created This LSAT-Flex Score Converter How to Take a Practice LSAT-Flex The LSAT Flex Score Predictor Conversion Table. How We Created This LSAT-Flex Score Converter. We’ve based this LSAT flex score converter table on the data available from the LSAC. Here are the key facts you should know about it: The LSAT-Flex has three sections instead of five like the standard in-person LSAT. It’s missing the experimental section (which doesn’t affect your score) and one of the two Logical Reasoning sections (which does impact how your raw score will translate to a scaled score). The LSAC has stated that the three sections of the LSAC Flex will “have about the same number of questions”. On the standard LSAT, there’s a range of questions you could technically encounter on each section: 23-24 Logic Games questions, 24-26 Logical Reasoning questions (x2 sections), and 26-28 Reading Comprehension questions. The LSAT-Flex exam uses previously administered questions from non-disclosed tests (meaning not from tests that have been released to the public as practice tests), and for the purposes of calculating estimated scores here, we’re making the assumption that sections will be kept intact and that LSAC won’t mix and match questions from sections from different exams (which would lead to different numbers of questions in a section from what usually appears). We could be wrong on this, but it feels likely that they would use complete sections for accuracy in score equating, but this is not confirmed! Even though there is a range of possible numbers of questions, most of the time Logic Games sections have 23 questions, Logical Reasoning sections have 25 questions, and Reading Comprehension sections have 27 questions. However, on the June LSAT-Flex administrations, some students did receive 26-question Logical Reasoning sections and some received 25-question LR sections, so it could vary. Based on what we’ve seen so far, we’ve based this score conversion chart presuming a 75- or 76-question LSAT-Flex Test . The actual range could potentially be anywhere between 73 to 78 questions, and this would skew the scoring a bit. Here’s a tip if you take a practice test for the LSAT Flex that has 73 or 74 questions or 77 or 78 questions: if you take a test that ends up with fewer overall questions (than the standard 75-76 we’ve mapped out below), count up your number of correct answers and match it to a scaled score that is one or two scores higher than the chart below. If you take a test with more overall questions, count your number of correct answers and match it to a scaled score that is one or two scores lower than the chart below. If you’re curious about the actual LSAT-Flex experience from a student’s perspective, check out our LSAT-Flex Review and Tips video. How to Take a Practice LSAT-Flex. Take a practice LSAT test, but skip one of the Logical Reasoning sections. Ideally, if you want to use the score conversion chart below precisely, you’ll take one that has a 23-question Logic Games section, a 25- or 26-question Logical Reasoning section, and a 27-question Reading Comprehension section (again, this is going to be most exams). It’s fine to use tests that have different numbers of questions, but take that into account when you use the chart below to convert your score if you do take a test that has fewer or more questions. Exciting news! In Magoosh LSAT Prep, you can take ready-made LSAT-Flex versions of several official LSAT exams! The LSAT Flex Score Predictor Conversion Table. Remember! This table assumes a 75- or 76-question LSAT-Flex (and technically, the actual range could be 73-78 questions). This is based on May scoring data, and the conversion for tests going forward may be adjusted slightly. This should be good enough for you to predict your LSAT-Flex score from practice though! For more on LSAT-Flex and how to prepare, check out our post on taking the LSAT-Flex at home. Remember this assumes a 75- or 76-question LSAT-Flex. LSAT Flex Raw Score LSAT Scaled Score LSAT Percentile 75-76 180 99.9 74 179 99.9 73 177 99.8 72 175 99.5 71 174 99.2 70 173 99.8 69 172 98.4 68 171 97.8 67 170 97.1 66 169 95.9 65 168 94.8 64 167 93.3 63 166 91.5 62 165 89.8 61 165 89.8 60 164 87.4 59 163 85.0 58 163 85.0 57 162 82.7 56 161 80.1 55 161 80.1 54 160 77.0 53 159 73.6 52 159 73.6 51 158 70.4 50 157 66.5 49 157 66.5 48 156 62.9 47 155 59.2 46 155 59.2 45 154 55.5 44 153 51.7 43 153 51.7 42 152 47.7 41 151 44.0 40 150 39.7 39 150 39.7 38 149 36.3 37 148 32.8 36 147 29.1 35 146 26.0 34 146 26.0 33 145 22.9 32 144 20.0 31 143 17.4 30 142 14.8 29 141 12.6 28 140 10.8 27 139 9.0 26 138 7.5 25 136 4.9 24 135 4.2 23 134 3.6 22 132 2.4 21 131 2.0 20 129 1.4 19 127 0.8 18 125 0.7 17 123 0.5 16 121 0.3 0-15 120 0.0. If you’re getting started on your LSAT prep journey, Magoosh has a free official LSAT practice test you can take that is also have formmatted as an LSAT-Flex test. Plus it comes with full video explanations for every question!
How to Select the Right Shaft Flex for a Golf Club. Selecting the right flex for your golf club shafts is key to getting the most out of your clubs. Shafts come in five flex categories: extra stiff (X), stiff (S), regular (R), senior (A) and ladies (L). Generally, the faster your swing the speed, the stiffer the shaft you will need. Selection. Step 1. Visit a golf store or fitting professional to have your swing evaluated. A professional will be able to assess your swing and speed and recommend the proper flex. Step 2. Extra-stiff flex may be used by golfers who consistently hit the ball 300 yards off the tee. This shaft is generally reserved for professional golfers. Step 3. Stiff flex may be used by golfers who consistently drive the ball 250 yards. This is one of the two most popular stiffnesses and is usually used by mid-low handicappers. Step 4. Regular flex may be used by golfers who consistently drive the ball between 225-250 yards and is also a very popular selection. This stiffness is appropriate for mid-high handicappers. Step 5. Step 6. Ladies flex may be used by women and those who consistently drive the ball less than 200 yards. This is the softest flex available.
Football Predictions. Football Predictions of Today, February 04. Premier League Predictions. Tottenham vs Chelsea Prediction. Match Time & Date: All eyes will be on Tottenham Hotspur Stadium when Tottenham and Chelsea go head to head in the London derby. Spurs failed to impress in a 1-0 loss to Brighton, with key player Harry Kane being sorely missed. The Tottenham ace remains sidelined with injury, and in his absence a man to watch in the home team will be Korea international Son. Both Ndombele and Dele Alli should . Read More. This match will be played on 04/02/2021 at 23:00. Portugal Primeira Liga Predictions. Farense vs Santa Clara Prediction. Match Time & Date: Estadio de Sao Luis will host Thursday’s football game between Farense and Santa Clara. Farense aim to make amends for back-to-back defeats to Porto and Tondela, and they will surely give 100% to escape the rock-bottom position in the standings. Boss Sergio Vieira should have a full squad to choose from against Santa Clara. Daniel Ramos’ men, on the other hand, aim to bu . Read More. This match will be played on 04/02/2021 at 18:00. Belenenses vs Porto Prediction. Match Time & Date: Belenenses will be looking to make amends for a 2-0 loss to Santa Clara when they take on Porto in the Primeira Liga round 17. O Belem’s main goal is to secure the top flight survival, but they will have to be at their sharpest in the remainder of the season if they are to meet the goal. Varela remains in the recovery room for the hosts, while Kau is rated doubtful for the . Read More. This match will be played on 04/02/2021 at 22:00. Braga vs Porto Prediction. Match Time & Date: Braga will be looking to build on a thrilling 4-0 win over Moreirense when they take on Portimonense at their Estadio Municipal de Braga. Carlos Carvalhal’s troops are eager to cement their spot in top three, and we are positive that they will go all guns blazing at Portimonense. Both Fonte and Moura are sidelined with injuries, but there are no suspended players in the ho . Read More. This match will be played on 05/02/2021 at 00:00. Famalicao vs Moreirense Prediction. Match Time & Date: Famalicao and Moreirense go head to head at Estadio Municipal de Famalicao in what is expected to be one of the most exciting games of Thursday’s program. Famalicao aim to bounce back from back-to-back defeats to Vitoria Guimaraes and Nacional, and we are positive that they will fight tooth and nail against Moreirense. Jorge Silas’ troops, though, are not regarded as goo . Read More. This match will be played on 05/02/2021 at 00:30. Copa del Rey Predictions. Betis vs Athletic Bilbao Prediction. Match Time & Date: There will be fireworks when Real Betis and Athletic Club face each other at Estadio Benito Villamarin in the Copa del Rey quarter-finals. Los Beticos did well to beat Real Sociedad 3-1 after extra-time in the last 16, and they are likely to stick to their attacking style of play in Thursday’s football match. Even though Bartra, Bravo, Camarasa, Martin, and Rodriguez remai . Read More. This match will be played on 04/02/2021 at 23:00. Today’s football predictions. The world of football is a pretty complex place, and as such, there are a lot of variables to enjoying the game. The majority of us tend to just watch and marvel at the amazement of these teams and players on a week to week basis, but sometimes, that isn’t the only way forward. A lot of viewers also like to register their own predictions and football tips on games, and as such, this page is going to be of good use to anyone who fits into that category. This site, and page specifically, allows you to read through all of the predictions for every single major game on a day to day basis. If there are no primary games taking place on the day that you happen to stumble upon the site, then don’t worry – as we’ll also have predictions for the upcoming days too. From domestic football to the international game, we’ve got fans covered on all fronts . If you wish to navigate your way through, feel free to use the calendar. This will allow you to browse a series of different leagues and games in order to get a wider spectrum of what is available. Long story short: if there’s a big game on, we’ll be here to cover it . Visitors can also enjoy our latest addition; Bet of the Day, where you can grab our best betting tip of the day! Which football leagues do we cover? There are so many leagues out there for fans to bet on and predict on and this stage, that you won’t be surprised to discover we also cover the majority of them. We primarily discuss the six biggest leagues (in our mind) that we’re going to go on to discuss now, and all of them have unique traits and attributes that we believe make them fascinating to watch. Premier League – The English Premier League is considered by many to be the most entertaining division of football on the planet, and it’s kind of hard to argue against that. Many of the world’s top clubs compete there, and when it comes to the finances involved, it’s pretty difficult to say they aren’t at the top of the list. Manchester United are obviously considered to be the biggest name, but alongside them you’ve got the likes of Liverpool, Manchester City, Chelsea and so many more. La Liga or Primera Division – Perhaps the greatest footballer we’ve ever seen, Lionel Messi , has been calling La Liga home with Barcelona for longer than we can remember. As such that kind of puts them forward as one of the primary frontrunners in the debate of ‘best league in the world’, and the actual style of play certainly suits that theory. Things are a bit slower over there with Barca, Real Madrid and Atletico Madrid often dominating proceedings, but they certainly know how to get results. Serie A – The Italian league is on the rise after spending a few years in the doldrums, and it is being led by the surging juggernaut of Juventus. They’ve been consistently reigning supreme for almost a decade now, and in the last twelve months, they’ve had Cristiano Ronaldo guiding the way. You’ve also got both Milan clubs who are steeped in history, as well as Napoli, Lazio and Roma. Bundesliga – Football is a simple game: you run around for 90 minutes, and then at the end, the Germans win. That’s the famous saying, and while it hasn’t been as true in the last few years as it has in previous generations, they’re still located towards the top of the food chain. The Bundesliga clubs have some of the most insane fans we’ve ever seen, with Borussia Dortmund and Bayern Munich continuing to set the standard at the top . Ligue 1 – Ligue 1 is the home nation of the current world champions France, which should tell you all that you need to know regarding the talent they produce over there. The quality of the league itself isn’t considered to be on the same level as some of the alternatives we’ve already listed, but even so, it’s hard not to marvel at the excellence of Paris Saint-Germain after what they’ve been able to accomplish in recent times. Champions League – The pinnacle of European football. The Champions League brings together the biggest and best teams from throughout the continent, in a competition that allows them to battle it out and determine who really does stand at the top of the mountain. Real Madrid have dominated in recent years , but beyond that, the quality of football is always just so remarkable to watch. Europa League – For the teams who are just on the edge of greatness, the Europa League provides them with a chance to prove that they can do battle with other sides from across Europe. The winner of this competition is able to make their way into the UCL the following season, which is a big enough incentive for anyone. Historically, the Spanish teams seem to thrive here. We don't stop covering only these six leagues. Each season we add new leagues giving our visitors the most accurate football tips and predictions out there. During summer, when regular leagues are in a break, we cover the most important tournaments as well . Our team of experts. Our team of experts don’t just focus on one specific player, team or league, and instead, they correlate data from every corner of the football world to make up some of the best predictions you’ll find throughout the land. They’re so precise and careful with the ideas they put forward, which is why they’re so heavily trusted and so highly regarded. We keep getting bigger and better with each and every passing day, and every week provides us with a new opportunity to make magic happen. Where to place bets? When you look for top of the range betting companies, especially in the United Kingdom, then you tend to start with bet365 and work your way down – as you can probably see from the following bookmaker reviews on our site. This can vary depending on what part of the world you originate from as there are different perks with different bookmakers, but by following that link you’ll be able to get a good idea of what’s out there . Free bets. The beauty of free bets is that they tend to come in all different shapes and sizes – and from all different kinds of bookmakers, too. If you head on over to our free bets page you’ll see a series of opportunities available to you depending on the country you’re based in, from new customer offers to free bets and so much more.
Monday Night Football odds, line: Cardinals vs. Cowboys picks, predictions from NFL expert on 14-4 roll. Zack Cimini has his finger on the pulse of the Cowboys and Cardinals. The Arizona Cardinals are looking to stick it to their old NFC East rivals when they visit AT&T Stadium to take on the Dallas Cowboys on Monday Night Football. The teams played in the East together for more than 30 years before realignment for the 2002 season moved the Cardinals to the NFC West. The Cowboys hold a 56-32-1 mark all-time, including a 32-11 record in Dallas. Arizona quarterback Kyler Murray and top receiver DeAndre Hopkins make a potent combination, while Cowboys quarterback Andy Dalton has plenty of talent all around him as he replaces the injured Dak Prescott (ankle). Kickoff is set for 8:15 p.m. ET at AT&T Stadium in Dallas. Dallas is a one-point favorite in the latest Cardinals vs. Cowboys NFL odds from William Hill, while the over-under is 55. Before making any Cowboys vs. Cardinals picks, be sure to see the latest Monday Night Football predictions from SportsLine expert Zack Cimini. Cimini is a highly respected Las Vegas handicapper who's never afraid to buck conventional wisdom. He also has an uncanny feel for the tendencies of both of these teams, going 6-1 on his last seven picks involving the Cardinals and 14-4 on against the spread picks for or against Arizona or Dallas. Anyone who has consistently followed him is way up. Now, he has studied Cardinals vs. Cowboys from every angle and released a confident against-the-spread pick. You can only see it here. Here are the NFL odds and betting trends for Cowboys vs. Cardinals: Why the Cardinals can cover. Hopkins and Murray both have the speed to exploit the struggling Dallas defense. Reigning Offensive Rookie of the Year Murray has thrown for 1,299 yards and run for 265 while accounting for 13 touchdowns. Hopkins, acquired in an offseason trade with the Texans, led the league in both receptions (45) and yards (528) entering the week, and 29 of his receptions have gone for first downs. Veteran Larry Fitzgerald, with 18 catches for 123 yards, Christian Kirk, and Andy Isabella also are reliable options for Murray. The Cardinals are 8-2-2 against the spread in their last 12 road games, and the defense has been hit with injuries but ranks 10th in the league in total yards allowed (346.6) and fifth against the pass (222.4). A secondary with cornerback Patrick Peterson and safety Budda Baker can make game-changing plays. Why the Cowboys can cover. Dallas is likely to lean on running back Ezekiel Elliott with Prescott out. The fifth-year pro has rushed for 364 yards and is third on the team with 24 catches for 173 yards. He also leads Big D in scoring with six touchdowns. Dalton went 70-61-2 in his nine seasons with the Bengals and led the winning drive against New York, with a 38-yard completion to Michael Gallup to set up the winning field goal. Gallup is one of numerous top receiving options for the Cowboys, who are 4-0 against the spread in their last four games as home underdogs. The wideout had 57 of his 73 yards on the final drive, while rookie CeeDee Lamb had 124 yards on eight catches. No. 1 option Amari Cooper had just two catches in the game but has a team-high 39 for 424 yards. The defense is yielding 404.4 yards per game but a rejuvenated Aldon Smith, who has four sacks, could give Murray trouble. How to make Cardinals vs. Cowboys picks. Cimini is leaning Over on the total, but his much stronger play is on the spread. He's found a critical X-factor that makes one side of the spread hit hard on Monday. He's only sharing it at SportsLine. Who wins Cowboys vs. Cardinals on Monday Night Football? And what critical X-factor makes one side of the spread a must-back? Visit SportsLine now to see which side of the Cardinals vs. Cowboys spread you should jump on Monday, all from the expert who's 14-4 on picks involving Arizona or Dallas.
Monday Night Football Preview. Bills at Patriots. Each week, TheLines will provide a betting breakdown and analysis for the upcoming Monday Night Football game. We’ll look at the odds and see why they’re moving a certain way, along with breaking down the matchup and providing the week’s best prop bets. The Buffalo Bills and New England Patriots will face off at Gillette Stadium for a Week 16 AFC East showdown on Monday night, Dec. 28. The game marks the 122nd meeting between the teams in a series the Patriots lead 76-44-1. The most recent game between the teams unfolded in Week 8 of this season, when Buffalo recorded a 24-21 win at New Era Field. In that contest, the Bills’ Josh Allen threw for just 154 yards and an interception, but the backfield duo of Devin Singletary and Zack Moss combined for 167 rushing yards and two touchdowns (Moss) on 28 carries. Stefon Diggs also found a way to be productive despite the lackluster day for the passing attack, compiling a solid 6-92 line. MNF odds and betting breakdown. Buffalo Bills at New England Patriots – 8:15 p.m. ET. The two teams come into this divisional clash with diametrically opposite outlooks for what remains of the regular season. For its part, Buffalo already has the AFC East crown in its pocket, having clinched it with a blowout victory over the Broncos in Week 15. Meanwhile, the Patriots are officially out of the postseason after having fallen to the Miami Dolphins on the road last Sunday. The Bills can still potentially improve their overall seeding, so coach Sean McDermott will presumably approach this game with a competitive mindset regardless of his team’s guaranteed postseason spot. The history of the line for this game reflects the belief of the oddsmakers and betting public that Buffalo has both more talent and motivation than its opponent. The line opened at Bills -4.5 late last week, and it subsequently bumped up to 6.5 after Week 15 results. It’s climbed another half-point to a full touchdown advantage for the visitors as of Friday afternoon. It is currently Bills . The only notable short-term injury concern (the Bills’ John Brown and the Patriots’ Julian Edelman both appear to be trending toward remaining on injured reserve for at least another week) for either team comes in the form of Patriots running back Damien Harris , who’s listed as questionable as of Friday afternoon with the ankle issue that cost him the Week 15 contest against Miami. The projected total for the contest has had very modest movement in comparison with the point spread. It sat at 45.5 points late Sunday and has only inched up another half-point since then, putting it a point over the final number for the first meeting between these clubs. The Over has a particularly poor record in New England’s home games this season, lending credence to the notion this could be a lower-scoring matchup. The total is currently . The Bills are 9-5 (64.3%) against the spread this season, including 3-3 as a road team and 1-3 (25.0%) in division matchups. The Over is 9-4-1 (69.2%) in Buffalo’s games this season, including 4-3 (57.1%) in its road games and 3-1 (75.0%) in its division matchups. The Patriots are 6-8 (42.9%) against the spread this season, including 4-2 (66.7%t) as a home team and 2-2 in AFC East matchups. Then, the Over is 4-10 (28.6%) in New England’s games this season, including 1-5 (16.7%) in its games as a home team and 2-2 against AFC East opponents. MNF prop search tool. Looking to bet on props for Monday Night Football? Use our prop search tool to find the best odds across legal sportsbooks in the US. Bills vs. Patriots matchup. Despite being without Brown for the last four games, Allen has shown impressive adaptability and has posted an 8:1 TD:INT while throwing for 972 yards over the last three contests specifically. Those types of numbers aren’t always the norm against what has been a stingy Patriots secondary this season, but New England’s loss of Stephon Gilmore for the rest of the season with a quadriceps injury suffered against the Dolphins could change that narrative somewhat this week. When Gilmore missed three games earlier this season with a knee injury, the Pats surrendered a combined five touchdown passes to the duo of the Ravens’ Lamar Jackson and the Jets’ Joe Flacco , so there could certainly be opportunities for a red-hot Allen. There’s an even more favorable matchup on paper for the Bills on the ground. Miami’s Salvon Ahmed and Matt Breida just combined for 208 yards and a rushing TD against New England’s front seven in Week 15, while Singletary and Moss found plenty of running room in the first meeting between these teams, as alluded to earlier. The Pats check in allowing the sixth-most rushing yards per game (133.0) and RB yards per carry (4.70), while New England also gives up the third-most adjusted line yards per tote (4.88). On the other side is Cam Newton – who appears set to remain in the top job under center despite the Patriots no longer having any postseason aspirations. New England’s passing game has been uneven all season under Newton and with Edelman missing the last eight contests. The Bills have given up their share of yards through the air, but they’ve tightened up at the right time of the season by giving up just 202.7 passing yards per game over the last three contests. Buffalo’s pass defense DVOA is up to a respectable No. 15 ranking as a result, placing the Bills just in the top half of the league. Meanwhile, opposing ground games have also found success on occasion against Buffalo, which has dealt with some injuries at the linebacker position this season that have contributed to some inconsistency against the run. Currently, the Bills check in allowing 123.9 rushing yards per game on the road, along with bottom-half figures of 4.50 adjusted line yards and 4.44 RB yards per carry allowed. If Harris is able to return for this game and work in tandem with Sony Michel , coach Bill Belichick could opt to lean heavily on the run in the hopes of protecting Newton, who sports an unsightly 5:10 TD:INT, has committed six fumbles (one lost) and has taken 31 sacks. The Bills defense has been one of the league’s best at taking the ball, posting 12 interceptions and 10 fumble recoveries to place them at No. 4 in the league in total turnovers forced.
Monday Night Football – Odds, Picks and Predictions – Pittsburgh Steelers vs. New York Giants. It hasn’t been a great Week 1 for road teams and some bettors could be fading that angle tonight when they make their MNF Picks on the Steelers and Giants. While Green Bay and Seattle took care of business, road teams as a whole are 5-8-1 against the spread heading into Monday Night Football. Large road favorites like Philadelphia and Indianapolis lost outright, so that might be a sign of things to come tonight with the Pittsburgh Steelers. Pittsburgh is a bit less than a touchdown favorite per the NFL betting odds tonight against the New York Giants in the first game of an ESPN doubleheader at MetLife Stadium in East Rutherford, New Jersey. Steelers vs. Giants MNF Betting Odds. The Steelers were one of the most popular teams in terms of NFL futures bets before the start of the season. Some of that is because of the franchise’s fanbase, and some is due to the return of Ben Roethlisberger. This offense completely let down a top five defense last season, leading to Pittsburgh missing out on the postseason. Handicapping MNF Steelers Giants. Steelers MNF Analysis. Welcome Back Big Ben! Big Ben should be a breath of fresh air for the Steelers. He threw for an NFL-high 5,129 yards in 2018 before suffering a season-ending injury in Week 2 last year, causing the offense to spiral. Pittsburgh ranked 30 th in terms of its passing games as neither Devlin Hodges nor Mason Rudolph could carry the load. If Roethlisberger is even 75 percent of what he was before his injury, the Steelers can threaten the Baltimore Ravens in the AFC North. His return should lead to JuJu Smith-Schuster becoming one of the better receivers in the AFC once more after a dismal 2019 campaign. While he was hurt, Diontae Johnson and James Washington emerged as solid ancillary threats in this passing game, and the addition of Eric Ebron gives Pittsburgh the pass-catching tight end it has lacked since the retirement of Heath Miller. Looking for a Back. The run game stands to be better too. Defenses rolled eight into the box when it became obvious Pittsburgh couldn’t throw the ball effectively, leading to underwhelming seasons for both James Conner and Benny Snell Jr. This is a good run-blocking offensive line when everyone is healthy, but unfortunately, the Steelers won’t have their best offensive lineman in Week 1 as right guard David DeCastro was declared out. The Giants did a great job of stopping the run last season, allowing just 3.9 YPC. Leonard Williams, Dalvin Tomlinson, and Dexter Lawrence are all very good pieces upfront in a 3-4 defense, but the secondary is still a mess. The pass defense was ranked in the bottom five, and it didn’t get any better in the offseason as the starting cornerbacks will be James Bradberry and Corey Ballentine. NFL betting links. Guide to NFL Point Spreads List of available No Risk Bets Best sites for NFL online betting. Giants MNF Analysis. Dime Play. Daniel Jones had a better rookie season than most people expected in 2019. Danny Dimes threw for over 3,000 yards and tossed twice as many touchdowns as interceptions, but over half his touchdown passes came in three games against bad defenses like Detroit, Washington, and the Jets. He is a much better athlete than his detractors give him credit for and that gives this offense another dimension. Jones averaged 6.2 YPC, so don’t be surprised to see some RPO action with Saquon Barkley and that could have you leaning to NY in your MNF Steelers Giants Picks. Ground and Pound. Barkley surpassed 1,000 yards in just 13 games, averaging 4.6 YPC. His production was very impressive with a rookie quarterback in the backfield as opposing defenses were loading up to stop the one proven offensive weapon the Giants had on the field. He wasn’t as much of a receiving threat as some projected he would be though with just 52 reception out of the backfield. Injuries took their toll on the Giants receiving corps as Evan Engram, Sterling Shepard, and Golden Tate all missed at least five games last year. Darius Slayton was the team’s leading receiver, but to be honest, none of these options strike fear into the heart of opponents. However, Pittsburgh’s defense does keep opposing offenses up at night. The Steelers had a superb defense last year with three First Team All-Pros in Cam Heyward, T.J. Watt, and Minkah Fitzpatrick. Trading for Fitzpatrick really shored up a secondary that has had some issues over the years, and the linebackers were superb too. Devin Bush could be one of the best linebackers in the game this year. MNF Picks Steelers vs. Giants. The over is a solid bet in our MNF Picks Steelers Giants, as we believe Pittsburgh should be able to move the ball effectively through the air, and the New York football team will do enough on offense. Score : Pittsburgh 31 New York 24 Best Bet : Over 44.5 at BetMGM Best Prop Pick : Ben Roethlisberger Over 285.5 Passing Yards at BetMGM.
A Simple Introduction to Tennis Scoring for Beginners. Learn the Basics for Playing a Tennis Match. Bob Thomas/Getty Images. Learning how to keep track of the score in a tennis match is not as difficult as it may seem. To put it simply: Four points wins the game Six games wins the set Two (or, on occasion, three) sets wins the match. But learning how to tally the points—and to keep track of the score during a fast-paced match—can seem daunting if you are a beginner. Read on, and you'll understand and keep up with the game in no time. Starting a Game. If you win a coin toss or a spin of the racquet, you get to choose whether you serve or receive the serve. If you choose to serve, your opponent gets to pick which end of the court to start on. This may seem like a small concession, but if the sun is shining in your eyes, the starting position can definitely influence the outcome of the game. To serve, you position yourself at the right side of the back of the court, which is called the baseline. If you serve first, your opponent must return the ball after exactly one bounce into any part of the singles section of your court. You and your opponent then continue to return the ball back and forth—which is known as a volley—until either one of you misses the ball, the ball bounces more than once on one side of the court, or the ball is hit out of bounds. If you miss, or your shot bounces or goes out of bounds, your opponent wins the point, and vice versa. Scoring Points. If you win the first point, you must announce the score at your next serve: "15–love." Love is equal to zero, and 15 equals one point. The server -- in this case, you -- always announces his or her own score first. Additional points are counted in increments of 15 up to two points, then the next point is scored as 10—yes, we know it doesn't make any sense, but it's the way the sport works. You will continue to serve until someone wins the game. So, if your opponent happens to win the next point, you will announce "15–all" before your next serve, meaning you and your opponent are tied. If your opponent wins the next point as well, you would announce "15–30." You will continue to serve until one of you wins the game. Two-Point Advantage. So what constitutes a win in a game of tennis? Simple: one of you has to win by two points. Say your opponent wins the point after you are up 40–30, the score would then be tied, and you would announce: "40–all," otherwise known as "deuce." Now you continue to play until one of you has a two-point advantage and wins the game. On televised games, this is the point at which you'll notice that the scoreboard changes. Points are no longer calculated. Instead, players either have the "advantage" (i.e., are one point away from two points), or they are tied in a "deuce." That's why some games seem to go on forever. Until one player achieves a two-point advantage, the game will go on. and on. But, that's also what makes tennis exciting. Once you have won six games, you've won a set. But, wait, you're still not finished. Starting a New Set. If the previous set ended with an odd-numbered total of games, you and your opponent switch sides of the court to begin the new set. You switch ends after every odd game through each set. At the start of a new set, in the above example, you served first. So, your opponent would get to serve to start the new set. In men's professional tennis, players generally must win three out of five sets to win a match. In women's professional tennis, players generally must win two out of three sets to win the match. If you're a beginner, do yourself a favor: Whether you're male or female, decide that the victor will be the player who wins two out of three sets. Your tired feet—and potential tennis elbow—will thank you.
Correct score betting. A correct score betting market lists the possible final scores for a particular sporting fixture. Odds are assigned to each of these. The odds roughly reflect the statistical chances of the game or match ending with the score in question. Punters bet on the scores they think will be on the final scoreboard. Because the odds on offer are so high, it’s usually possible to bet on several different correct score options and still earn an overall profit if one of the bets wins. When is correct score betting offered? Correct score betting is most often associated with football betting. However, it may be offered on any sport in which scores are low enough to make it practical for bookmakers to provide odds for all the possible score outcomes. This includes sports like tennis, squash and hockey. Correct score betting tends to offer much higher odds than regular match betting markets. Football examples. Example 1: Chelsea takes on Barcelona in the Champions League. A bookmaker offers a selection of odds in the correct score market, ranging from odds of 10/1 for a 1-0 win to Chelsea to odds of 500/1 for a 10-0 win to Barcelona. A punter predicts that the match will be won 2-1 by Chelsea, an option that’s priced by the bookmaker at 11/2. The final score of the match is 2-1 to Barcelona, so the bet loses. Example 2: France plays Italy at the European Championships. A bookmaker offers odds ranging from 5/1 for a 1-1 draw to 500/1 for a 10-0 win to Italy. After researching the form of both sides, a punter decides to bank on the match producing a 1-1 draw. The final score of the match is indeed 1-1, so the punter’s bet is paid out at odds of 5/1. Correct score tennis betting. In tennis, correct score betting is usually offered on the outcome of a specific set. Some bookmakers also offer in-running betting on the score for a specific game. Set betting is another variation of correct score betting. In this case, you bet on the number of sets that will be won by the match winner versus the number won by the match loser. Correct score betting in tennis is worth exploring – a good understanding of the form of the participants and their recent results means that one can predict set scores with some degree of accuracy. Example : Andy Murray takes on Rafael Nadal in a tennis match. Odds are offered on the full possible selection of results for the third set. A punter bets on the set being a close one, and takes the 15/1 odds offered on the set being won 7-5 by Nadal. Nadal wins the set 7-5, so the bet is paid out at 15/1. When to bet on correct scores. Correct score betting is always a high-risk option, which is why the odds offered on these markets are so good. However, there are times when these types of bets can be made with a little more accuracy. Generally, it’s not advisable to place a single correct score bet on a market. Instead place a selection of correct score bets, spreading your stake around while ensuring that you’ll still make an overall profit if one of your bets wins. For example, say Chelsea is due to play Barcelona. Odds of 11/2 are offered on a score 2-1 to Chelsea, odds of 12/1 are offered on a 2-0 score to Chelsea and odds of 10/1 are offered on a score of 1-1. You bet £10 on all three options for a total stake of £30. If any one of these bets wins, you'll make an overall profit. What to take into account. Factors you should take into account when considering a correct score bet include: Form: Especially in football, teams tend to get stuck in grooves and will consistently win matches by a one-goal margin or feature in low-scoring fixtures. If you see this sort of trend emerge, you can narrow down your betting options. Relative strength: It’s important to assess the relative strength of both sides; results between equally matched sides will tend to fall into a narrower margin than those between a strong side and a weaker one. Weather: The weather typically affects the number of scores in a football match. Look out for matches played in poor weather conditions because these are likely to produce lower scores, and this narrows the margin of likely final scores.
Why Is Tennis Scored So Weirdly? All sports have their own vocabularies, the shorthand lingo to communicate intricacies of rules and how play proceeds. But usually the scoring can at least be counted on to be fairly straightforward. Not so much for tennis. For the unfamiliar, tennis starts with both players at zero, called love: “Love-all.” One person scores: 15 to love. The server’s score is said first, the receiver’s second. The other now scores, and they’re tied at “15-all.” The next point is 30, then 40, and the following point wins that game. If they tie at 40 it’s called a deuce. From that tie the next person to get a point has the advantage, but generally has to win by two points — that is, to score twice in a row — to win the game. And it doesn’t stop there. Six of these games make a set, and the set must be won by two games or it goes to a tiebreaker. After the set is over, it repeats. To win the whole match requires either winning best of five sets or best of three sets, depending on the competition. As tennis fans get ready to watch stars like Serena Williams, Roger Federer, Novak Djokovic and Naomi Osaka play at the U.S. Open, those less familiar with the game may once again ponder an inevitable question: Why count this way? Disappointingly, the origins of pretty much every part of the scoring system are a mystery. “I don’t think anybody really knows how it started or why it developed how it did,” says Elizabeth Wilson, who wrote Love Game: A History of Tennis, from Victorian Pastime to Global Phenomenon . “There are various theories, all sorts of romantic theories have been built up about it.” says Wilson. “That’s partly what makes tennis into a kind of romantic game, because it had all this history that isn’t really history, it’s legend more than actual history. Some of the ideas about how it began are quite fanciful.” The Points. Despite its complexity, the tennis scoring system has been stable since the Victorian period. The modern game of tennis traces back to a medieval game called jeu de paume , which began in 12th century France. It was initially played with the palm of the hand, and rackets were added by 16th century. With its strong association with pageant traditions of the French court, Wilson says, tennis was highly stylized from the beginning. Over a course of the next few centuries the game saw periods of incredible popularity, with more than 1,000 tennis courts in Paris in the 16th century. Though it’s well known for being beloved among royalty (Henry VIII was a notable and avid player, and the French revolutionaries’ tennis court oath was made on an indoor court at Versailles), it was also enjoyed among commoners and monks. Records of scoring systems related to today’s date back almost to the beginning of the sport, but in these years the scoring was 15, 30, 45 — the math of which at least makes more sense than the modern system, as each increment was 15 points. A poem written a few years after the 1415 battle of Agincourt counts up the points — 15, 30, 45 — in a tennis game between English King Henry V and the French Dauphin. Charles d’Orléans composed a ballad around 1439 while imprisoned in an English castle after the battle of Agincourt, in which he compared life with a game of tennis and uses the French word for 45, playing on the number as both his age and the score in a tennis match. A 1505 tennis match at Windsor castle gave one player a handicap of 15. Around the 1520s Erasmus wrote a dialogue between two tennis players where one says “we’ve got 30, we’ve got 45.” Though one 16th-century English text did use 40 for a tennis score, a treatise from a few decades earlier indicated that French students were simply shortening the word “45” to “40” when they described the game (and their teacher corrected that it should be 45), writes Heiner Gillmeister, linguistics scholar at Bonn University in Germany and sports historian, who authored the thoroughly researched Cultural History of Tennis in 1998. Get our History Newsletter. Put today's news in context and see highlights from the archives. Thank you! But the reasons behind this counting method were obscure even then. (There’s plenty to speculate about where the English word tennis originates, too, but it definitely emerged sometime in the 15th century.) In the 1520s, for example, one Jan van den Berghe had questions: “What was not explained was how players can win fifteen points for a single stroke. It is, after all, a little curious that they count or win more than one point for a single stroke… Why is not one point given for one stroke, and two for two strokes?” Over the centuries, various theories have relied on everything from complicated multiplication to the history of scoring systems in other games to measurements of distances between lines of demarcation on early courts, but no definite answer could be found. One of the most common suggestions, Wilson says, is that the progression is related to minutes on a clock. “It’s been suggested that the monks would look at the clock, the 4 points and somehow felt that was a good way of scoring,” she says, “and then 45 became 40, though nobody knows why.” Clocks were rapidly developing in the Middle Ages and division into a rough quarter hour was imaginable. This theory’s detractors point out that minute hands on clocks were only introduced in the late 1500s and became common even later. The Terms. As for “love,” the word has been used since the 1700s to mean “nothing” and is also used in a variety of other games from racket sports to cards (including bridge and whist). But how it came to mean this is also unexplained. One often repeated option traces the etymology to the French l’oeuf , meaning egg, an object the same shape as the number 0. But there is no indication the French ever used l’oeuf in relation to tennis scoring, writes American tennis player Malcolm D. Whitman in his 1932 book Tennis: Origins and Mysteries , and they didn’t write scores down, so the visual association wouldn’t cue the egg comparison. Gillmeister also writes that “love” is not how that type of loan word would be modified into English — Latin’s bovem became the French boeuf and turned into beef in English, so l’oeuf would likely have become something sounding more like leaf if that theory had held true. Gillmeister has a different loan-word idea. Perhaps it’s from the Dutch or Flemish lof , meaning honor, which would have made sense if players saw a tennis match as akin to a battle. (“Deuce” is a clearer loan word — deux is French for “two” — but the mechanism or timing of that transition is less clear.) Or maybe it’s not a loan word at all: phrases along the lines of “neither for love nor money” had already entered the lexicon, according to Gillmeister. So the idea that a person with “love” had no money could be a plausible option as to why that might be the word for having no points in a game that was a frequent subject of wagers. The Side Effects. By the 1800s, tennis’ popularity was in decline. The game we know as tennis today grew out of an adaptation called lawn tennis to distinguish it from the older indoor version, “real” or royal tennis. (In the U.S. that older version goes by “court tennis.”) In the 1870s, Major Walter Clopton Wingfield published rules for that new game, and a few others developed a similar game elsewhere in England at the time. Played outdoors, the court was hourglass shape and points were counted one by one. But when the All England Croquet Club set a field aside for the new game and held a championship in 1877 — the first Wimbledon Championship — they combined the new and old rules. Part of that change was a reversion to the “real” or medieval tennis scoring system: 15, 30, 40. Lawn tennis, which was associated with the upper classes, “could afford to be more ornate, have these refinements, these quirks,” Wilson says. Even as competition increased, it remained a social spectacle. Those associations “perpetuate the retention of this weird scoring system,” says Wilson. “It becomes part of the glamour, people who watch tennis, who know tennis, can understand the scoring system, which is a bit obscure. That gives it more cachet, chutzpah, more glamour again in a funny way.” The rules for scoring have remained almost entirely static ever since, despite some attempts to simplify it. In 1966, for example TIME quoted James Van Alen, then president of the tennis Hall of Fame, as blaming the scoring system for the fact that “the players outnumber the spectators” in American tennis. Perhaps, the story mused, “the International Lawn Tennis Federation, which controls amateur tennis, will fall out of love with love.” As it turns out no such luck, though one part of Van Alen’s proposed revised scoring system — tiebreakers — was added in the 1970s. “Somewhere in this vast, great nation, there undoubtedly is a strong, agile, fiercely competitive youngster who could be the best tennis player the world has ever seen. This youngster himself may never know it. Or even care,” TIME observed in 1967. “Little that surrounds the game of tennis today is likely to appeal to him much. For a starter, there is the scoring system.”
Betting on Tennis (Correct Score Sets) I like to bet on tennis games because there are fewer factors that are decisive for this sport in my view. In this sport there are only two possible outcomes that boosts chances of winning. You can’t say is same thing in soccer (2 way bets) when you play asian handicap because strong teams that have big handicap (AH -1.5 , AH -1.25) are not always interested to win by a big margin. Tennis players are interested to finish their games as fast as possible and rest. You depend just on one player to win you bet not on an entire team. Is better to bet on big tournaments where players have bigger motivation to win. To help me decide easier which bets to take I use these sites: ATP Tour Steve G Tennis WTA Tour Match Stat and tournament official site. My strategy : My bets are usually correct score sets : 2-0 (or 3-0 in big tournaments). To do that bet I select games from bookmaker’s offer. Step 1) I like to bet on strong favourites to win with straight odds about 1.20. 1.50 (but odds aren’t main factor for choosing my bet) and I select from offer games with indicated odds ; Step 3) now exclusion part: at first step I selected almost blindly games (only looking at odd). From games I selected at step 1, I check whether all favourite players ( favourite is made by bookmaker ) are strong enough on surface that is played. — I exclude players with poor performances on playing surface (false favourite): I check winnig /losing record on this surface and current form (he is losing/winning lately ? winning gives to a player peace); — search for serve percentages , average serving speed or returning bals percentage won (he is a fighter) Odds for straight win were about 1.2. 1.5 but if that player is really favourite he should be able to win in straight sets : 2-0 ( or 3-0 ) and odds for correct score are bigger: from 1.2 at straight win , at least 1.6 for 2-0 ; from 1.4. 1.5 at straight win , at least 1.9. Money management : fixed amount of money bet evey time. Check bookmaker tennis rules. Sometimes players are injured and they have to retire. Is better to place bets on bookmakers with “ match completed ” rule. My statistic (average) from 78 games with 2-0 and 3-0 bets: 53 winning bets, average odds 1.87 25 losing bets, average odds 1.95.
Correct score per set tennis. Please purchase or remove the membership in your cart. Please try logging back in or call customer support at 1-800-990-8782 for assistance. Profile update. Please update your profile. About To Expire Expired. Your Safe Play Approval Expires in $(daysToExpire) days! Your Safe Play Approval has expired! About To Expire Expired. Your Admin status expires in $(daysToExpire) days! Your Organization Admin is expired! About To Expire Expired. Your Membership Expires in $(daysToExpire) days! Your Membership has expired! Please complete your account creation. You're in SECTION / DISTRICT. Share your location for a more tailored experience. Scoring Points & Tennis Sets. Powered by USTA Officiating. Tennis is a sport where “love” means zero, and the scoring system is different for games, sets and matches. We get it: that's confusing. Thanks to the USTA Officiating department, it doesn't have to be. This handy guide will teach you the basics of scoring, and playing the sport for a lifetime, with helpful tips and buzzwords that you may want to know before you take to the court. Mastering the proper terminology may not help your forehand or serve, but at least in conversation, you can hang with anyone. THE BASICS. OVERVIEW. Tennis is a game played on a rectangular-shaped court, which can be one of many surfaces. It is either played with two players (singles match), or four players (doubles match). Players stand on opposite sides of the net and use a stringed racquet to hit the ball back and forth to each other. GAMEPLAY. Each player or team has a maximum of one bounce after the ball has been hit by their opponent to return the ball over the net and within the boundaries of the court. In wheelchair tennis, players get a maximum of two bounces. When a player then fails to return the ball into the correct court, the opponent wins a point. The aim of tennis is to win enough points to win a game, enough games to win a set, and enough sets to win a match. STARTING THE MATCH. Before warming up with your opponent, either player or team will spin their racquet and the winner of the spin will have some options to choose from. They can choose one of these 3 options: To serve or receive The side of the court Or defer their choice to their opponent --but the opponent cannot defer back. Once the winner of the toss chooses one of the options above, the opponent has the remaining choice. SCORING A GAME. POINT SYSTEM. Tennis has a different point system than most sports. Before we go into detail, here is your guide to scoring a game: 0 points= Love 1 point = 15 2 points= 30 3 points= 40 Tied score= All 40-40 = Deuce Server wins deuce point = Ad-In Receiver wins deuce point = Ad-Out. WHO WINS? In order to win the game, a player must win at least four points. If you are up 40-30, 40-15 or 40-love, and win one more point, you win the game. If the score is tied in a game or set, you use the term “ all ” when announcing the score. For example, if you and your opponent have both won two points in the game, the score would be 30-all. WHAT IS A DEUCE? The only time this is different is when both you and your opponent have won 4 points each and the score is 40-40. This is called deuce . When the score reaches deuce, one player or team will need to win at least two points in a row to win the game. When the server wins the deuce point, it is called Ad-In, but when they lose the deuce point, it is called Ad-Out. If the team with the advantage (Ad-In or Ad-Out) wins another point, they win the game, or it goes back to deuce. SWITCHING ENDS. Players or teams switch ends of the court on odd games. This means that after the first game is complete, they switch sides, as well as every two games after that. TYPES OF SETS. Now let’s look at how many games you need to win a set. There are two main ways of scoring a set. ADVANTAGE SET. In an advantage set, a player or team needs to win six games, by two, to win the set. This means that there is no tiebreak game played at 6-6. The set continues until one player/team wins by two games. TIEBREAK SET. In a tiebreak set, a player or team needs to win six games wins a set. If the score gets to 5-5 (5-all), one player must win the next two games to win the set. If the score reaches 6-6 (6-all) in the set, a tiebreak game is played. SCORING A TIEBREAK GAME. In a tiebreak game, the next person who was due to serve will start the tiebreak game, and serve one point to the deuce side of the court. The following two points will then be served by the opponent starting on the ad side. In doubles, the player on the opposing team due to serve will serve these points. Players or teams switch ends of the court every six points (e.g. when the score is 4-2), and to score this tiebreak game, you use, “zero” “one”, “two”, “three”, etc. The first player or team to win seven points , by two, wins the tiebreak. This means the score can end up being very high (e.g. 15-13) or as low as 7-0 through 7-5. Whoever wins the tiebreak game, wins the set by a score of 7-6.
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Where can I get my credit score? You actually have more than one credit score. Credit scores are calculated based on the information in your credit reports. If the information about you in the credit reports of the three large consumer reporting companies is different, your credit score from each of the companies will be different. Lenders also use slightly different credit scores for different types of loans. There are four main ways to get a credit score: Check your credit card or other loan statement. Many major credit card companies and some auto loan companies have begun to provide credit scores for all their customers on a monthly basis. The score is usually listed on your monthly statement, or can be found by logging in to your account online. Talk to a non-profit counselor. Non-profit credit counselors and HUD-approved housing counselors can often provide you with a free credit report and score and help you review them. Use a credit score service. Many services and websites advertise a “free credit score.” Some sites may be funded through advertising and not charge a fee. Other sites may require that you sign up for a credit monitoring service with a monthly subscription fee in order to get your “free” score. These services are often advertised as “free” trials, but if you don’t cancel within the specified period (often as short as one week), you could be on the hook for a monthly fee. Before you sign up to try one of these services, be sure you know what you are signing up for and how much it really costs. . Other services may also offer scores for purchase. If you decide to purchase a credit score, you are not required to purchase credit protection, identity theft monitoring, or other services that may be offered at the same time. Some credit score sources provide an "educational" credit score, instead of a score that a lender would use. The CFPB published a report on the differences between educational scores and those used by lenders. . For most people, an educational score will be close to the scores lenders use and can be helpful for consumers. But the scores can be quite different for some. Our report found a meaningful difference for one out of four people. When choosing where to get your credit score, find out what kind of score it is. . Your credit scores are calculated based on the information in your credit reports. If there are errors on your credit reports, they can reduce your scores unnecessarily.
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We basically have two different approaches. The first approach is mathematical, through the use of mathematical rules and formulas to create game systems that bring the player to the cashier. We can also call it the most nerdy approach. That point of view that only a fan tends to exploit. The second approach is a little less mathematical but certainly no less methodical. You make predictions based on your knowledge of the teams that will take the field. It is an approach that relies heavily on factor c, which is luck. In the first case, however, what the player tries to do is eliminate luck as much as possible to try to be certain on the correct score.
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Football Predictions. Football Predictions of Today, February 04. Premier League Predictions. Tottenham vs Chelsea Prediction. Match Time & Date: All eyes will be on Tottenham Hotspur Stadium when Tottenham and Chelsea go head to head in the London derby. Spurs failed to impress in a 1-0 loss to Brighton, with key player Harry Kane being sorely missed. The Tottenham ace remains sidelined with injury, and in his absence a man to watch in the home team will be Korea international Son. Both Ndombele and Dele Alli should . Read More. This match will be played on 05/02/2021 at 01:00. Portugal Primeira Liga Predictions. Farense vs Santa Clara Prediction. Match Time & Date: Estadio de Sao Luis will host Thursday’s football game between Farense and Santa Clara. Farense aim to make amends for back-to-back defeats to Porto and Tondela, and they will surely give 100% to escape the rock-bottom position in the standings. Boss Sergio Vieira should have a full squad to choose from against Santa Clara. Daniel Ramos’ men, on the other hand, aim to bu . Read More. This match will be played on 04/02/2021 at 20:00. Belenenses vs Porto Prediction. Match Time & Date: Belenenses will be looking to make amends for a 2-0 loss to Santa Clara when they take on Porto in the Primeira Liga round 17. O Belem’s main goal is to secure the top flight survival, but they will have to be at their sharpest in the remainder of the season if they are to meet the goal. Varela remains in the recovery room for the hosts, while Kau is rated doubtful for the . Read More. This match will be played on 05/02/2021 at 00:00. Braga vs Portimonense Prediction. Match Time & Date: Braga will be looking to build on a thrilling 4-0 win over Moreirense when they take on Portimonense at their Estadio Municipal de Braga. Carlos Carvalhal’s troops are eager to cement their spot in top three, and we are positive that they will go all guns blazing at Portimonense. Both Fonte and Moura are sidelined with injuries, but there are no suspended players in the ho . Read More. This match will be played on 05/02/2021 at 02:00. Famalicao vs Moreirense Prediction. Match Time & Date: Famalicao and Moreirense go head to head at Estadio Municipal de Famalicao in what is expected to be one of the most exciting games of Thursday’s program. Famalicao aim to bounce back from back-to-back defeats to Vitoria Guimaraes and Nacional, and we are positive that they will fight tooth and nail against Moreirense. Jorge Silas’ troops, though, are not regarded as goo . Read More. This match will be played on 05/02/2021 at 02:30. Copa del Rey Predictions. Betis vs Athletic Bilbao Prediction. Match Time & Date: There will be fireworks when Real Betis and Athletic Club face each other at Estadio Benito Villamarin in the Copa del Rey quarter-finals. Los Beticos did well to beat Real Sociedad 3-1 after extra-time in the last 16, and they are likely to stick to their attacking style of play in Thursday’s football match. Even though Bartra, Bravo, Camarasa, Martin, and Rodriguez remai . Read More. This match will be played on 05/02/2021 at 01:00. Today’s football predictions. The world of football is a pretty complex place, and as such, there are a lot of variables to enjoying the game. The majority of us tend to just watch and marvel at the amazement of these teams and players on a week to week basis, but sometimes, that isn’t the only way forward. A lot of viewers also like to register their own predictions and football tips on games, and as such, this page is going to be of good use to anyone who fits into that category. This site, and page specifically, allows you to read through all of the predictions for every single major game on a day to day basis. If there are no primary games taking place on the day that you happen to stumble upon the site, then don’t worry – as we’ll also have predictions for the upcoming days too. From domestic football to the international game, we’ve got fans covered on all fronts . If you wish to navigate your way through, feel free to use the calendar. This will allow you to browse a series of different leagues and games in order to get a wider spectrum of what is available. Long story short: if there’s a big game on, we’ll be here to cover it . Visitors can also enjoy our latest addition; Bet of the Day, where you can grab our best betting tip of the day! Which football leagues do we cover? There are so many leagues out there for fans to bet on and predict on and this stage, that you won’t be surprised to discover we also cover the majority of them. We primarily discuss the six biggest leagues (in our mind) that we’re going to go on to discuss now, and all of them have unique traits and attributes that we believe make them fascinating to watch. Premier League – The English Premier League is considered by many to be the most entertaining division of football on the planet, and it’s kind of hard to argue against that. Many of the world’s top clubs compete there, and when it comes to the finances involved, it’s pretty difficult to say they aren’t at the top of the list. Manchester United are obviously considered to be the biggest name, but alongside them you’ve got the likes of Liverpool, Manchester City, Chelsea and so many more. La Liga or Primera Division – Perhaps the greatest footballer we’ve ever seen, Lionel Messi , has been calling La Liga home with Barcelona for longer than we can remember. As such that kind of puts them forward as one of the primary frontrunners in the debate of ‘best league in the world’, and the actual style of play certainly suits that theory. Things are a bit slower over there with Barca, Real Madrid and Atletico Madrid often dominating proceedings, but they certainly know how to get results. Serie A – The Italian league is on the rise after spending a few years in the doldrums, and it is being led by the surging juggernaut of Juventus. They’ve been consistently reigning supreme for almost a decade now, and in the last twelve months, they’ve had Cristiano Ronaldo guiding the way. You’ve also got both Milan clubs who are steeped in history, as well as Napoli, Lazio and Roma. Bundesliga – Football is a simple game: you run around for 90 minutes, and then at the end, the Germans win. That’s the famous saying, and while it hasn’t been as true in the last few years as it has in previous generations, they’re still located towards the top of the food chain. The Bundesliga clubs have some of the most insane fans we’ve ever seen, with Borussia Dortmund and Bayern Munich continuing to set the standard at the top . Ligue 1 – Ligue 1 is the home nation of the current world champions France, which should tell you all that you need to know regarding the talent they produce over there. The quality of the league itself isn’t considered to be on the same level as some of the alternatives we’ve already listed, but even so, it’s hard not to marvel at the excellence of Paris Saint-Germain after what they’ve been able to accomplish in recent times. Champions League – The pinnacle of European football. The Champions League brings together the biggest and best teams from throughout the continent, in a competition that allows them to battle it out and determine who really does stand at the top of the mountain. Real Madrid have dominated in recent years , but beyond that, the quality of football is always just so remarkable to watch. Europa League – For the teams who are just on the edge of greatness, the Europa League provides them with a chance to prove that they can do battle with other sides from across Europe. The winner of this competition is able to make their way into the UCL the following season, which is a big enough incentive for anyone. Historically, the Spanish teams seem to thrive here. We don't stop covering only these six leagues. Each season we add new leagues giving our visitors the most accurate football tips and predictions out there. During summer, when regular leagues are in a break, we cover the most important tournaments as well . Our team of experts. Our team of experts don’t just focus on one specific player, team or league, and instead, they correlate data from every corner of the football world to make up some of the best predictions you’ll find throughout the land. They’re so precise and careful with the ideas they put forward, which is why they’re so heavily trusted and so highly regarded. We keep getting bigger and better with each and every passing day, and every week provides us with a new opportunity to make magic happen. Where to place bets? When you look for top of the range betting companies, especially in the United Kingdom, then you tend to start with bet365 and work your way down – as you can probably see from the following bookmaker reviews on our site. This can vary depending on what part of the world you originate from as there are different perks with different bookmakers, but by following that link you’ll be able to get a good idea of what’s out there . Free bets. The beauty of free bets is that they tend to come in all different shapes and sizes – and from all different kinds of bookmakers, too. If you head on over to our free bets page you’ll see a series of opportunities available to you depending on the country you’re based in, from new customer offers to free bets and so much more.
Predicting Football Results With Statistical Modelling. Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution. David Sheehan. Data scientist interested in sports, politics and Simpsons references. Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house. Poisson Distribution. HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1. We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches). You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions. We can use this statistical model to estimate the probability of specfic events. The probability of a draw is simply the sum of the events where the two teams score the same amount of goals. Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution. So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively). Building A Model. You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?). Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421. If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense. Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.
Predicting Football Results With Statistical Modelling. Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution. David Sheehan. Data scientist interested in sports, politics and Simpsons references. Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house. Poisson Distribution. HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1. We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches). You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions. We can use this statistical model to estimate the probability of specfic events. The probability of a draw is simply the sum of the events where the two teams score the same amount of goals. Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution. So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively). Building A Model. You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?). Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421. If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense. Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.
Predicting Football Results With Statistical Modelling. Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution. David Sheehan. Data scientist interested in sports, politics and Simpsons references. Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house. Poisson Distribution. HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1. We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches). You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions. We can use this statistical model to estimate the probability of specfic events. The probability of a draw is simply the sum of the events where the two teams score the same amount of goals. Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution. So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively). Building A Model. You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?). Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421. If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense. Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.
Predicting Football Results With Statistical Modelling. Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution. David Sheehan. Data scientist interested in sports, politics and Simpsons references. Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house. Poisson Distribution. HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1. We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches). You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions. We can use this statistical model to estimate the probability of specfic events. The probability of a draw is simply the sum of the events where the two teams score the same amount of goals. Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution. So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively). Building A Model. You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?). Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421. If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense. Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.
Predicting Football Results With Statistical Modelling. Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution. David Sheehan. Data scientist interested in sports, politics and Simpsons references. Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house. Poisson Distribution. HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1. We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches). You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer: represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions. We can use this statistical model to estimate the probability of specfic events. The probability of a draw is simply the sum of the events where the two teams score the same amount of goals. Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution. So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively). Building A Model. You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?). Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421. If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense. Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.
