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.
Fix problems with apps from Microsoft Store. If you're in Windows 10 and you're having problems with an app from Microsoft Store, consider these updates and fixes. First, sign in to your Microsoft account. Next, work through these possible solutions in the order presented. Make sure Windows has the latest update: Select check for updates now, and then select Check for updates . Or, select the Start button, then select Settings > Update & Security > Windows Update > Check for Updates . If there is an available update, select Install now . Make sure that your app works with Windows 10. For more info, see Your app doesn't work with Windows 10. Update Microsoft Store: Select the Start button, and then from the apps list, select Microsoft Store . In Microsoft Store, select See more > Downloads and updates > Get updates . If an update for Microsoft Store is available, it will start installing automatically. Troubleshoot games: If you're having issues installing a game, see Troubleshoot game installations on Windows 10. Reinstall your apps: In Microsoft Store, select See more > My Library . Select the app you want to reinstall, and then select Install . Run the troubleshooter: Select the Start button, and then select Settings > Update & Security > Troubleshoot , and then from the list select Windows Store apps > Run the troubleshooter. Here's more help. If you're having trouble launching Microsoft Store, see Microsoft Store doesn't launch. If you can launch the Microsoft Store but you are just having trouble finding or installing an app, see I can't find or install an app from Microsoft Store.
15 Ways to Fix Microsoft Store Not Downloading Apps or Games Issue. Gaurav Bidasaria. 19 Nov 2019. Microsoft Store (earlier known as Windows Store) is the official destination for Windows 10 apps. So, what happens when Microsoft Store stops downloading apps or games? While some developers do allow users to download the executable file directly from their site, not everyone does. More and more developers are listing their apps on the Microsoft Store. It makes it so much easier to find and download apps. It’s also a great way to push updates and find new customers. Let’s see what we can do. We will begin with some simple solutions first. 1. Update OS. You knew this was coming. Press Windows key+I to open Settings and click on Updates & Security. That’s where you can look for new OS updates and install them. These are also recommended for security reasons. 2. Relaunch Store, Reboot Computer. This is a simple trick but sometimes works. It may happen due to a glitch or some random connection error. Close Microsoft Store and relaunch it to see if the download is working again or not. You may try rebooting your computer once before trying. 3. Internet Settings. 4. Pending Downloads. Are there any pending downloads in the Microsoft Store? That is a common reason for delays. Your download won’t start until previous tasks are completed. Usually, Microsoft Store downloads 2-3 apps simultaneously but that may not be the case always. Open Microsoft Store and click on menu to open Downloads and updates. You can see all pending and available downloads here. See if anything is in queue. If you are in a hurry, cancel other downloads to give priority to the one that you need. Also on Guiding Tech. 6 Best Tricks for Using Microsoft Store on Your Windows 10 Computer. 5. Date and Time. It’s a rare one but may happen to anyone. Check the date and time on your computer to make sure that it is correct. If not, that could be the reason why Microsoft Store is functioning erratically. In fact, wrong date and time often break apps that connect to the internet. Right-click on time and select Adjust date/time to open settings and change it from there. You can also set it to automatic or manual, depending on which one is not working. 6. Storage Space. For a computer, Windows can be pretty dumb sometimes. Consider this. If you don’t have enough storage space in the download drive, you thought that Microsoft Store would calculate that before starting a download? Nope. It will begin the download and then stop halfway when it runs out of space. Well, you know what to do now. See the next point for info on how to change app/game location. 7. Change Download Location. Here’s another reason to change your download location before starting a download. It is known to help with drive related errors. Open Settings again and click on Storage. Select Change where new content is saved. This is where you can change your default download location, often C: drive, to a different drive. 8. Troubleshooting. You can troubleshoot a lot of built-in features and apps. Open Settings and search for Find and fix problems with Microsoft Store Apps. This will also help with the store itself, as it is also an app. Click on Apply repairs automatically and select Next to continue. Follow on-screen instructions after this. You can also Find and fix network problems if you are facing it, using troubleshoot. Another troubleshoot you should run is Windows Update which is closely related with Microsoft Store. Search for Find and fix problems with Windows Update. Also on Guiding Tech. Windows 10 App Store Crashes at Launch: Try These 7 Fixes. 9. Antivirus and Malware. Run an antivirus scan to make sure that your computer is not infected. It can not only steal your data but also break apps and system settings. Download and run Malwarebytes if you don’t already have it on your machine. 10. Clear Cache. Open Start menu and type ‘wsreset’ without quotes. Click on Run as administrator to run this command. Click Yes if you are asked to give admin rights. You will see the Command Prompt launch briefly. It’s a window with a black background. This will clear Microsoft Store cache. You may have to sign back in. Do it and see if it fixes Microsoft Store not downloading apps or games error. 11. Reset Microsoft Store. Open Settings and click on Apps. Search for Microsoft Store in the list and click on the name to reveal Advanced options. Scroll a little and you will see the Reset button. It is not required but I would recommend a reboot at this point. Check if app or games download are still pending or stuck in Microsoft Store. 12. Clean Boot and Safe Mode. There may be an app conflict which is responsible for Microsoft Store not downloading apps or games. Try both these methods to see if you can find the culprit. 13. Re-Register Microsoft Store. Search for and open Powershell with admin rights from the Search menu. Enter the command below to re-register Microsoft Store on your computer. This can solve a lot of problems associated with the app. 14. Reset Windows Update. Type Services in the Start menu and click on Run as administrator. Find Windows Update service, right-click on it and select Stop. Open File Explorer and drill down to the folder structure below. You are looking for a folder named SoftwareDistribution. Rename it to SoftwareDistribution.old. Go back to the Services windows and restart the Windows Update service. Just like you stopped it. Try downloading that app or game from Microsoft Store again. 15. Restore or Reset Windows. Try restoring your computer if nothing else works. Try resetting your computer if restore doesn’t work. These two steps are often recommended as a last resort when nothing else solves a critical error or issue. Fix it Up. Microsoft support has compiled a short list of troubleshooting steps if you are unable to install games in particular. These steps won't work for apps. Do try them if nothing mentioned above works in your favor. As always, if you found another way or something does work, do share with us in the comments below. Next up: If you can download apps from Microsoft Store but not getting desired speeds, click on the link below to learn what to do. Last updated on 24 Nov, 2019 The above article may contain affiliate links which help support Guiding Tech. However, it does not affect our editorial integrity. The content remains unbiased and authentic. Read Next. Top 4 Ways to Fix Microsoft Store Missing in Windows 10. Can't find the Microsoft Store (or Windows Store) on your Windows 10 computer? Check out four (4) ways to get it fixed. How to Fix You'll Need the Internet for This Microsoft Store Error. Connected to the internet but are greeted with 'You'll need an internet connection for this' in the # Microsoft Store? Here are some possible solutions. 6 Best Tricks for Using Microsoft Store on Your Windows 10 Computer. Here are 6 tips and tricks to use # Windows Store like a pro. Learn how to install, uninstall and reset apps, change download location of the app and more today. 7 Best Ways to Fix OneNote Won't Let Me Type Error in Windows 10. Can't type anything in # OneNote on a # Windows computer? We have compiled a list of solutions that should help you fix this error in no time. 7 Best Ways to Fix App Icons Not Showing in Taskbar on Windows 10. App icons not showing in the # Taskbar on # Windows 10 desktop? All the # troubleshooting steps for fixing and getting the app icons to show in the # Taskbar on # Windows 10. 6 Best 4K Video Players for Windows 10. Are you looking to play 4K videos on # Windows 10? Read the post to find the six best 4K # video players for # Windows 10. Top 5 Fixes for Windows 10 Battery Low Notification Not Working. Does your # Windows 10 laptop shut down without showing the low # battery warning? Check 5 ways to fix low battery notification not working on # Windows 10. 9 Best Ways to Fix Windows 10 Update Error. Can't update your # Windows 10 computer easily? Here are the best solutions to fix # Windows 10 update errors to ensure your computer runs the latest version. Did You Know. Windows Hello is a biometric authentication feature in Windows 10.
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Date Match Pick Odds FT W/L 01.01.2021 Sheffield Wed – Derby 1 2.80 1:0 WIN 01.01.2021 Everton – West Ham 2 3.20 0:1 WIN 02.01.2021 Hull – Charlton 1 2.20 2:0 WIN 02.01.2021 Plymouth – Gillingham 1 2.30 1:0 WIN 03.01.2021 Eibar – Granada 1 2.60 2:0 WIN 03.01.2021 Aue – Braunschweig 1 2.00 3:1 WIN 04.01.2021 Cosenza – Empoli 2 2.20 0:2 WIN 04.01.2021 Ascoli – Reggina 1 2.90 2:1 WIN 05.01.2021 Mansfield – Salford 1 2.50 2:1 WIN 05.01.2021 Sochaux – Dunkerque 1 2.00 1:0 WIN 06.01.2021 Metz – Bordeaux X 3.00 0:0 WIN 06.01.2021 Marseille – Montpellier 1 2.10 3:1 WIN 07.01.2021 Fenerbahce – Alanyaspor 1 2.00 2:1 WIN 07.01.2021 Ankaraspor – Adanaspor AS 2 2.60 0:2 WIN 08.01.2021 Charlton – Accrington 2 3.00 0:2 WIN 08.01.2021 Sochaux – Clermont X 2.80 0:0 WIN 09.01.2021 Bordeaux – Lorient 1 2.10 2:1 WIN 09.01.2021 Metz – Nice X 3.20 1:1 WIN 10.01.2021 Levante – Eibar 1 2.30 2:1 WIN 10.01.2021 Cadiz CF – Alaves X 2.90 3:1 Lose 11.01.2021 Elche – Getafe 2 2.20 1:3 WIN 11.01.2021 Huesca – Betis 2 2.50 0:2 WIN 12.01.2021 Atl. Madrid – Sevilla 1 2.00 2:0 WIN 12.01.2021 Granada – Osasuna 1 2.30 2:0 WIN 13.01.2021 Den Haag – Venlo 2 2.50 1:4 WIN 13.01.2021 Den Haag – Venlo 2 2.50 1:4 WIN 14.01.2021 Atromitos – Lamia 1 2.00 2:1 WIN 14.01.2021 Aris – AEK Athens 2 2.40 0:1 WIN 15.01.2021 Red Star – Boulogne X 2.90 0:0 WIN 15.01.2021 Vicenza – Frosinone X 2.90 0:0 WIN 16.01.2021 Burton – Ipswich X 3.40 0:1 Lose 16.01.2021 Fleetwood – Portsmouth X 3.30 0:1 Lose 17.01.2021 Cercle Brugge – St. Liege 2 2.00 0:1 WIN 17.01.2021 Gent – Antwerp 2 3.50 0:1 WIN 18.01.2021 Munich 1860 – Ingolstadt 1 2.50 1:0 WIN 18.01.2021 Portimonense – Belenenses 1 2.40 1:0 WIN 19.01.2021 Peterborough – Charlton 1 2.20 2:1 WIN 19.01.2021 Reading – Coventry 1 2.20 3:0 WIN 20.01.2021 Betis – Celta Vigo 1 2.20 2:1 WIN 20.01.2021 Rizespor – Gaziantep 1 3.00 3:0 WIN 21.01.2021 Ankaragucu – Kasimpasa 1 2.40 1:0 WIN 21.01.2021 Valencia – Osasuna X 3.10 1:1 WIN 22.01.2021 Stoke – Watford 2 2.40 1:2 WIN 22.01.2021 Entella – Pisa 1 2.90 2:1 WIN 23.01.2021 Freiburg – Stuttgart 1 2.70 2:1 WIN 24.01.2021 Reims – Brest 1 2.50 1:0 WIN 24.01.2021 Metz – Nantes 1 2.60 2:0 WIN 25.01.2021 Le Mans – Red Star 1 3.00 2:1 WIN 25.01.2021 Belenenses – Tondela 1 2.20 2:0 WIN 26.01.2021 Gillingham – Crewe 1 2.90 4:1 WIN 26.01.2021 Scunthorpe – Port Vale 1 2.70 2:0 WIN 27.01.2021 Coventry – Sheffield Wed 1 2.20 2:0 WIN 27.01.2021 Barnsley – Cardiff 1 2.50 2:2 Lose 28.01.2021 St. Pauli – Bochum 2 2.20 2:3 WIN 28.01.2021 Sion – Basel 2 2.00 2:3 WIN 29.01.2021 Reading – Bournemouth 1 3.00 3:1 WIN 29.01.2021 Colchester – Scunthorpe 2 2.90 0:1 WIN 30.01.2021 Sittard – Venlo 1 2.10 3:2 WIN 30.01.2021 Eupen – Mouscron X 3.30 1:1 WIN 31.01.2021. 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Best Correct Score Football Betting Sites. As the name of the bet suggests, a correct score bet is simply a wager on what the final score of any given football match will be. In this post I plan to cover the best betting sites for correct score betting along with how to win betting on the correct score of football matches more often. It can be pretty difficult to predict the correct score of any given football fixture but there are things you can do to improve your strike rate and win more often than you are at the moment. The first you need to do is sort out where you’re placing your bets and move to the bookmakers I suggest below. Quick Tip: If it’s actually correct score tips/predictions you’re looking for then the best place to check would be the cheeky punter tips section. Best Bookmakers for Correct Score Betting. The following is a list of the best bookmakers for correct score bets & why: Betvictor – best odds on football Bet365 – top bonus William Hill – acca insurance Betfred – most promotions Coral – weekly loyalty offers 888Sport – 300% welcome bonus. How to Win Betting on the Correct Score of Football Matches. Now you know my best betting site for correct score betting lets move on to how to win betting on the correct score of football matches. It’s not an exhaustive guide below but it should give you an idea of how you can up your strike rate with this bet type. Select the most common results – Statistics show that the average football match has around 2.6 goals in it. As such selecting scores that adhere to this principle means that statistically you have more chance of a winning correct score bet. Weight the home team in favour of the away – Home teams tend to outscore away teams by an average of around a goal per game, as such when selecting a score remember to give the home team a slight advantage. Of course, use common sense too, especially in cup matches where the away team may be markedly better than the home, but if you are deciding on a 2-1 or 1-2 scoreline, remember in most cases, statistically the home team tends to win the game. Don’t just rely on statistics – The beauty of football is that for all the statistics and analysis, there is always the chance that any game can completely be out of character. As such, use your knowledge of football too to help inform your bet. Check the form of each of the teams involved in the game, how many goals do the score and concede on average at home, or away from home. Do they have any players returning from injury who may help improve the team, or do they have players injured, suspended or otherwise not playing who may impact on the teams overall effectiveness? Check the history of the fixture – If you are serious about correct score betting strategy then you need to check the history of the fixture you’re betting on – this applies mainly to league fixtures. Historically some matches tend to be more high scoring affairs than others are. Remember though, current form is generally a better indicator for a correct score prediction than historical matches, but history is useful for providing a long-term context for the bet. What is the context of the game? – The context of the game can massively influence how each of the teams competing approach the game. If, for example, you are betting on the second leg of a European tie where one team needs to win the game by several goals, this will radically impact how they approach the game and could well lead to more goalscoring chances for both teams. Try and get inside the head of the manager to decide how the teams are likely to approach this game. Alternative Correct Score Bets. There are a couple of other correct score bets that are popular with punters. Half Time Correct Score. One is the half time correct score bet, which as the name suggests, is when a punter backs a scoreline that they feel will be reflected at half time. Obviously, this bet offers less scope for the higher scores (few games are 3-3 before the interval and a large proportion may have no, or just one goal. Half Time/Full Time Correct Score. The Half Time/Full Time Correct Score bet is in effect a double, where the punter backs what the score will be at half time and then doubles that with the full time score. These bets are very difficult to predict however and offer relatively little chance of success, however they do certainly offer the best odds available in the market with even wagers of 0-0 half time, 1-0 full time paying out at excellent odds.
Correct Score Prediction & Picks. Get all the information you need for placing bets on correct score Market. Correct Score Tips for Today. Tottenham vs Chelsea. Tottenham 0-2 Chelsea at 8/1 with 10 Bet. SC Farense vs Santa Clara. SC Farense 2-1 Santa Clara at 9/1 with Betway. Belenenses vs FC Porto. Belenenses 0-3 FC Porto at 15/2 with Bet365. Braga vs Portimonense. Braga 3-0 Portimonense at 8/1 with 888sports. FC Famalicao vs Moreirense. FC Famalicao 2-1 Moreirense at 15/2 with Ladbrokes. Recommended Free Bets & Bonus Offers For football. On First Deposit. For New Customers. View All. Correct Score 4th Feb 2021. Lorem ipsum dolor sit amet. Win Treble Tips. Both Teams To Score Tips. Fantasy Football Tips. Soccer Saturday Super 6. Upcoming Matches (Time as per GMT) Correct Score Betting Tips. Correct Score bets are some of the most popular football bets to place, and yet they are also some of the hardest to get right. As a result, the odds – and potential rewards – when you do can mean a decent pay-out in your favour. What is a Correct Score Bet? A Correct Score bet, unsurprisingly, is a single bet on the final score of a game. As mentioned, it is one of the hardest bets to get right, but as a result you will get good odds on each selection. Usually, the odds are shorter for lower scoring games – 0-0, 1-0, 0-1, 1-1 – and they increase gradually as you bet on more goals being scored. Why bet on Correct Score? While they are the hardest to get right, and you can often be waiting for some time for a payout, thanks to the increased odds available for a correct score bet the payouts are often well worth waiting for. Providing you bet sensibly, you only need a handful of correct scores to payout each season to finish the year in profit and, as we will cover below, there are plenty of ways to improve your odds of getting a correct score bet right. Correct Score Tips: Bet types and how to improve your potential returns. It is difficult to correctly predict the exact score of a football match, but there are many ways you can improve your chances of placing a successful bet. Study the form, look for games where less goals are likely to be scored and look at leagues where match outcomes tend to be easier to predict. Let’s take a closer look… What to look out for when betting on Correct Score. One of the first things you do when betting on the correct score of a football match is avoid picking games where you think plenty of goals will be scored. Of course, the bigger scorelines will return greater odds and more potential profit, but there’s a reason for that – it’s less likely to be successful. Even if you think a match will be a one-sided affair, with one team dominating, there are so many different variations of how that scoreline – even if you predicted the outcome (i.e. a big win for one team) correctly – could look. Anything, really, above a 3-0 win would be considered a big victory. Will the team step off the gas after building such a lead? Will the opposing team snatch a consolation? The number of scorelines that would be considered вЂhigh-scoring’ are vast, whereas if you correctly pick a low-scoring game, there are only really four outcomes to look out for. Low-scoring matches. Study the form book, and if you have found two teams playing each other who tend not to score – or concede – many goals, you have a good starting point for a successful correct score bet. For starters, a game with few goals will finish one of four ways – 0-0, 1-0, 0-1, 1-1. If you are correct in predicting the low-scoring game, there is a one-in-four chance, effectively, that you will then have picked the correct scoreline too. The odds are lower for those scorelines, but given 1,782 matches finished 1-0 to either side in the first 25 years of the Premier League – no other outcome occurred more than 1500 times – you can see there are going to be more payouts. For the record, in that period, 1,140 finished 1-1 and 828 were goalless draws. However, we would advise normally looking away from the Premier League (see below) due to the more unpredictable nature of the league compared to some of Europe’s other top divisions. Correctly identifying a low-scoring match also offers the chance of a consolation should you bet through Bet365 too – we’ve detailed how to take advantage of their "Bore Draw Money Back" offer below. But where should you be looking for these low-scoring matches? Regimented leagues. The Premier League is often viewed as being the best league in the world, by commentators prone to hyperbole, thanks to its unpredictable nature. High-scoring games are common and, as many managers like to point out вЂanyone can beat anyone’. Of course, that bit is true for every league, but in the Premier League it seems to ring true more often. Instead, you need to find leagues where low-scoring games are common – the German Bundesliga, for example. With teams known typically for their organised defences and regimented formations, just less than 25 per cent of all matches played before the winter break in the 2017/18 finished either 1-0 to the home team or 1-1. Both teams to score; over 2.5 goals in match. Another popular type of football bet is a combined BTTS/over 2.5 goals in match bet, and when predicting the correct score a 2-1 win is another result worth betting on. Indeed, in the first 25 years of the Premier League as we quoted above, the second most common scoreline was 2-1 – occurring 1,455 times. If you choose the BTTS/more than 2.5 goals in match bet, you will likely get slightly lower odds than if you specifically back a team to win 2-1 – and yet it is a relatively common scoreline. In La Liga, 14 per cent of matches in the 2017/18 season – at the time of writing – finished 2-1 to either the home or away team, while in the Scottish Premiership that total goes up to more than 18 per cent; on average, that means one team every weekend in the Scottish Premiership will win a game 2-1. And yet you can still get odds of around 8/1-9/1 of the favourites winning 2-1 in a typical Scottish Premiership match market. Picking a 2-1 scoreline can also keep your bet alive for longer than if you back a 1-0. No matter who scores first, your bet still has a chance of winning. It’s no use if it ultimately fails, but nobody wants to be reaching half-time or even earlier on match day having already had to tear up their betting slip. Pick a score, and stick with it. Once you have identified the potential low-scoring games, it is best to stick with a scoreline when placing correct score bets. Sure, the chance of every game you bet on finishing with that scoreline is extremely low, but some are likely too at least meaning you will get at least some payouts if you pick your games well. Constantly change your predicted score around and, in actual fact, you are reducing the probability of your correct score bet coming off. How can you take advantage of Bet365’s Bore Draw Money Back Offer? We have already mentioned the bonus of a consolation if you pick a low-scoring game through Bet365. Bet365 offer money back on any Correct Score, Half-Time/Full-Time or Scorecast bets if the match in question finishes 0-0. So if you have correctly identified a low-scoring game, that ends up finishing goalless, you will get your money back at least – even if your 1-0 Correct Score bet is therefore not successful.
Understanding Scaled Scores. Scaled scores are a type of exam score. They are commonly used by testing companies that administer high stakes exams, such as admissions, certification and licensure exams. Scaled scores are also used for K-12 Common Core testing and other exams that assess student skills and evaluate learning progress. Raw Scores vs. Scaled Scores. The first step to understanding scaled scores is to learn how they differ from raw scores. A raw score represents the number of exam questions you answer correctly. For example, if an exam has 100 questions, and you get 80 of them correct, your raw score is 80. Your percent-correct score, which is a type of raw score, is 80%, and your grade is a B-. A scaled score is a raw score that has been adjusted and converted to a standardized scale. If your raw score is 80 (because you got 80 out of 100 questions correct), that score is adjusted and converted into a scaled score. Raw scores can be converted linearly or nonlinearly. Scaled Score Example. The ACT is an example of an exam that uses linear transformation to convert raw scores to scaled scores. The following conversation chart shows how raw scores from each section of the ACT are transformed into scaled scores. The Equating Process. The scaling process creates a base scale that serves as a reference for another process known as equating. The equating process is necessary to account for differences between multiple versions of the same test. Although test makers try to keep the difficulty level of a test the same from one version to the next, differences are inevitable. Equating allows the test maker to statistically adjust scores so that the average performance on version one of the test is equal to average performance on version two of the test, version three of the test and so on. After undergoing both scaling and equating, scaled scores should be interchangeable and easily comparable no matter which version of the test was taken. Equating Example. Let's look at an example to see how the equating process can impact scaled scores on standardized tests. Imagine that say you and a friend are taking the SAT. You will both be taking the exam at the same test center, but you will be taking the test in January, and your friend will be taking the test in February. You have different testing dates, and there is no guarantee that you will both take the same version of the SAT. You may see one form of the test, while your friend sees another. Although both tests have similar content, the questions are not exactly the same. After taking the SAT, you and your friend get together and compare your results. You both got a raw score of 50 on the math section, but your scaled score is 710 and your friend's scaled score is 700. Your pal wonders what happened since both of you got the same number of questions correct. But the explanation is pretty simple; you each took a different version of the test, and your version was more difficult than his. To get the same scaled score on the SAT, he would have needed to answer more questions correctly than you. Test makers that use an equating process use a different formula to create a unique scale for each version of the exam. This means that there is no one raw-to-scale-score conversion chart that can be used for every version of the exam. That is why, in our previous example, a raw score of 50 was converted into 710 on one day and 700 on another day. Keep this in mind as you are taking practice tests and using conversion charts to transform your raw score into a scaled score. Purpose of Scaled Scores. Raw scores are definitely easier to calculate than scaled scores. But testing companies want to make sure that test scores can be fairly and accurately compared even if test takers take different versions, or forms, of the test on different dates. Scaled scores allow for accurate comparisons and ensure that people who took a more difficult test are not penalized, and people who took a less difficult test are not given an unfair advantage.
Correct Score Betting Type Explained. Buy Cheap MTN, Glo, 9Mobile, Airtel Data VTU Services SIZE VALID YOU PAY BUY 1GB 30DAYS ₦295 BUY 2GB 30DAYS ₦595 BUY 3GB 30DAYS ₦895 BUY 5GB 30DAYS ₦1495 BUY. you must be wondering what the betting type called correct score is all about.the correct score betting type is one of the most difficult betting type because you don't know if the outcome of the match will end in your favour.most at times their odds is usually high because the sport bookies knows what is at stake. online betting website such as nairabet, merrybet, bet365 etc, most time give high odds on correct score option due to the risk involved.Correct Score has to do with Predicting the exact score at the end of normal time (full time). Scores are quotes as "Home Team Score - Away Team Score" . Example: Bet Your type Score Odds manchester vs Chelsea, (Correct score) 1-1 0r 1-0 or 2-0 or 2-2 etc. You win only if the match ends in favour of the correct score you predicted, lets say maybe in a 1-1 draw, and lose if there is any other outcome at the end of the game. what this simply means is that, the score lines would have already been given by the sports bookies and you just have to choose the score line you see as the correct score.it could be 0-0, 1-0, 1-1, 2-0, 2-1, 2-2 etc.so if your prediction come through, you will be smiling to the bank.but any other outcome apart from the correct score you choose, then you have lost the game if you have not created NAIRABET ACCOUNT , CLICK HERE TO REGISTER FOR FREE. HERE ARE TOP BETTING SITES WITH WELCOME BONUS.
J-League. J League is highest football league in Japan and is founded in 1992 year, and also in one of the toughest football leagues in Asia. Contains 18 clubs which play 2 matches against each other - 1 as host and 1 as guest for a total of 34 matches per season. Clubs are ranked in table according to points they won. At the end of the season top 3 clubs receive the right to participate in AFC Champions League. The last two clubs are relegated to lower football league named J2 League, and their spots are taken by first two clubs from J2 League. The clubs finished on 16th place will play playoff against winner of J2 League play-off winner, and the winner from this match will take last free spot in J League for next season.
Japan j league correct score predictions. п»ї japan j league correct score predictions. J League is highest football league in Japan and is founded in 1992 year, and also in one of the toughest football leagues in Asia. Contains 18 clubs which play 2 matches against each other - 1 as host and 1 as guest for a total of 34 matches per season. Clubs are ranked in table according to points they won. At the end of the season top 3 clubs receive the right to participate in AFC Champions League. The last two clubs are relegated to lower football league named J2 League, and their spots are taken by first two clubs from J2 League. The clubs finished on 16th place will play playoff against winner of J2 League play-off winner, and the winner from this match will take last free spot in J League for next season.
Correct Score Fixed Matches. correct score fixed bets. CORRECT SINGLE FIXED TIP DATE : 12.07.2020 ( Sunday ) Aalesund – Bodo/Glimt ( NORWAY: Eliteserien ) TIP : 1 : 6 ( CS ) ODDS : 81.00 RESULT : 1 : 6 WIN. CORRECT SINGLE FIXED TIP DATE : 05.07.2020 ( Sunday ) Taby – Team TG FF ( SWEDEN: Division 1 – Norra ) TIP : 5 : 1 ( CS ) ODDS : 51.00 RESULT : 5 : 1 WIN. CORRECT SINGLE FIXED TIP DATE : 27.06.2020 ( Saturday ) Ludogorets – Lok. Plovdiv ( BULGARIA: Parva liga – Championship Group ) TIP : 6 : 1 ( CS ) ODDS : 81.00 RESULT : 6 : 1 WIN. CORRECT SINGLE FIXED TIP DATE : 21.06.2020 ( Sunday ) Bodo/Glimt – Haugesund ( NORWAY: Eliteserien ) TIP : 6 : 1 ( CS ) ODDS : 101.00 RESULT : 6 : 1 WIN. CORRECT SINGLE FIXED TIP DATE : 09.05.2020 ( Saturday ) NO CORRECT SCORE TIP TODAY. CORRECT SINGLE FIXED TIP DATE : 03.05.2020 ( Sunday ) NO CORRECT SCORE TIP TODAY. CORRECT SINGLE FIXED TIP DATE : 12.04.2020 ( Sunday ) Khujand – Khatlon ( TAJIKISTAN: Vysshaya Liga ) TIP : 2 : 3 ( CS ) ODDS : 81.00 RESULT : 2 : 3 WIN. CORRECT SINGLE FIXED TIP DATE : 05.04.2020 ( Sunday ) NO CORRECT SCORE TIP TODAY. SINGLE FIXED TIP DATE : 29.03.2020 ( Sunday ) NO CORRECT SCORE TIP TODAY. SINGLE FIXED TIP DATE : 21.03.2020 ( Saturday ) NO CORRECT SCORE TIP TODAY. SINGLE FIXED TIP DATE : 15.03.2020 ( Sunday ) NO CORRECT SCORE TIP TODAY. SINGLE FIXED TIP DATE : 07.03.2020 ( Saturday ) Brackley Town – Blyth ( ENGLAND: National League North ) TIP : 5 : 2 ( CS ) ODDS : 67.00 RESULT : 5 : 2 WIN. SINGLE FIXED TIP DATE : 01.03.2020 ( Sunday ) Kalmar – Jonkopings ( SWEDEN: Svenska Cupen ) TIP : 5 : 0 ( CS ) ODDS : 71.00 RESULT : 5 : 0 WIN. SINGLE FIXED TIP DATE : 22.02.2020 ( Saturday ) Dover – AFC Fylde ( ENGLAND: National League ) TIP : 5 : 1 ( CS ) ODDS : 81.00 RESULT : 5 : 1 WIN. SINGLE FIXED TIP DATE : 16.02.2020 ( Sunday ) Cremonese – Trapani ( ITALY: Serie B ) TIP : 5 : 0 ( CS ) ODDS : 101.00 RESULT : 5 : 0 WIN. SINGLE FIXED TIP DATE : 08.02.2020 ( Saturday ) Binacional – FBC Melgar ( PERU Liga 1 – Apertura ) TIP : 2 : 4 ( CS ) ODDS : 101.00 RESULT : 2 : 4 WIN. SINGLE FIXED TIP DATE : 01.02.2020 ( Saturday ) Sochaux – Lorient ( FRANCE: Ligue 2 ) TIP : 0 : 0 ( CS ) ODDS : 61.00 RESULT : 0 : 4 WIN. SINGLE FIXED TIP DATE : 28.01.2020 ( Tuesday ) FC Halifax – Dover ( ENGLAND: National League ) TIP : 4 : 2 ( CS ) ODDS : 76.00 RESULT : 4 : 2 WIN. SINGLE FIXED TIP DATE : 25.01.2020 ( Saturday ) Raith – Montrose ( SCOTLAND: League One ) TIP : 4 : 3 ( CS ) ODDS : 81.00 RESULT : 4 : 3 WIN. SINGLE FIXED TIP. DATE : 19.01.2020 ( Sunday ) Trabzonspor – Kasimpasa ( TURKEY: Super Lig ) TIP : 6 : 0 ( CS ) ODDS : 67.00 RESULT : 6 : 0 WIN. SINGLE FIXED TIP DATE : 11.01.2020 ( Saturday ) RW Essen (Ger) – Groningen (Ned) ( WORLD: Club Friendly ) TIP : 2 : 6 ( CS ) ODDS : 126.00 RESULT : 2 : 6 WIN. SINGLE FIXED TIP DATE : 05.01.2020 ( Sunday ) QPR – Swansea ( ENGLAND: FA Cup ) TIP : 5 : 1 ( CS ) ODDS : 81.00 RESULT : 5 : 1 WIN. SINGLE FIXED TIP DATE : 01.01.2020 ( Wednesday ) Bolton – Burton ( ENGLAND: League One ) TIP : 3 : 4 ( CS ) ODDS : 101.00 RESULT : 3 : 4 WIN. SINGLE FIXED TIP. DATE : 28.12.2019 ( Saturday ) Academica – Oliveirense ( PORTUGAL: LigaPro ) TIP : 4 : 3 ( CS ) ODDS : 126.00 RESULT : 4 : 3 WIN. SINGLE FIXED TIP. DATE : 21.12.2019 ( Saturday ) Fontinhas – Fatima ( PORTUGAL: Campeonato de Portugal ) TIP : 3 : 3 ( CS ) ODDS : 81.00 RESULT : 3 : 3 WIN. SINGLE FIXED TIP. DATE : 15.12.2019 ( Sunday ) Novara – Siena ( ITALY: Serie C – Group A ) TIP : 4 : 0 ( CS ) ODDS : 61.00 RESULT : 4 : 0 WIN. SINGLE FIXED TIP. DATE : 07.12.2019 ( Saturday ) Akhisarspor – Istanbulspor AS ( TURKEY: 1. Lig ) TIP : 4 : 3 ( CS ) ODDS : 81.00 RESULT : 4 : 3 WIN. SINGLE FIXED TIP. DATE : 30.11.2019 ( Saturday ) Paks – Ujpest ( HUNGARY: OTP Bank Liga ) TIP : 2 : 4 ( CS ) ODDS : 67.00 RESULT : 2 : 4 WIN. SINGLE FIXED TIP. DATE : 23.11.2019 ( Saturday ) Leyton Orient – Forest Green ( ENGLAND: League Two ) TIP : 2 : 4 ( CS ) ODDS : 67.00 RESULT : 2 : 4 WIN. SINGLE FIXED TIP. DATE : 16.11.2019 ( Saturday ) Altrincham – Hereford ( ENGLAND: National League North ) TIP : 5 : 1 ( CS ) ODDS : 67.00 RESULT : 5 : 1 WIN. SINGLE FIXED TIP DATE : 09.11.2019 ( Saturday ) Barnsley – Stoke ( ENGLAND: Championship ) TIP : 2 : 4 ( CS ) ODDS : 51.00 RESULT : 2 : 4 WIN. SINGLE FIXED TIP DATE : 02.11.2019 ( Saturday ) Hebei Elite – Jiangxi Liansheng ( China: Yi League ) TIP : 4 : 1 ( CS ) ODDS : 51.00 RESULT : 4 : 1 WIN. SINGLE FIXED TIP DATE : 27.10.2019 ( Sunday ) Doxa – Anorthosis ( CYPRUS First Division ) TIP : 0 : 6 ( CS ) ODDS : 81.00 RESULT : 0 : 6 WIN. SINGLE FIXED TIP DATE : 19.10.2019 ( Saturday ) AFC Fylde – Peterborough Sports ( ENGLAND: FA CUP ) TIP : 6 : 1 ( CS ) ODDS : 51.00 RESULT : 6 : 1 WIN. SINGLE FIXED TIP DATE : 12.10.2019 ( Saturday ) Queens Univ. – Dundela ( NORTHERN IRELAND: NIFL CHAMPIONSHIP ) TIP : 5 : 2 ( CS ) ODDS : 67.00 RESULT : 5 : 2 WIN. SINGLE FIXED TIP DATE : 05.10.2019 ( Saturday ) Montana – Pomorie ( BULGARIA: VTORA LIGA ) TIP : 5 : 1 ( CS ) ODDS : 61.00 RESULT : 5 : 1 WIN. SINGLE FIXED TIP DATE : 28.09.2019 ( Saturday ) Hereford – Truro ( ENGLAND: FA CUP ) TIP : 5 : 2 ( CS ) ODDS : 81.00 RESULT : 5 : 3 WIN. SINGLE FIXED TIP DATE : 21.09.2019 ( Saturday ) Asane – Baerum Sportsklubb ( NORWAY: DIVISION 2 – GROUP 2 ) TIP : 6 : 1 ( CS ) ODDS : 61.00 RESULT : 6 : 1 WIN. SINGLE FIXED TIP DATE : 14.09.2019 ( Saturday ) The Strongest – Real Potosi ( BOLIVIA: DIVISION PROFESIONAL ) TIP : 6 : 2 ( CS ) ODDS : 81.00 RESULT : 6 : 2 WIN. SINGLE FIXED TIP DATE : 07.09.2019 ( Saturday ) Legion – Elva ( ESTONIA: ESILIIGA ) TIP : 5 : 3 ( CS ) ODDS : 67.00 RESULT : 5 : 3 WIN. SINGLE FIXED TIP DATE : 01.09.2019 ( Sunday ) Maritimo B – Chaves B ( PORTUGAL: CAMPEONATO DE PORTUGAL ) TIP : 5 : 3 ( CS ) ODDS : 81.00 RESULT : 5 : 3 WIN. correct score fixed bets.
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Here are genuine past track records as follow: DATE MATCH TIP ODD 2021-02-02 Accrington – Bristol Rovers 6:1 150.00 2021-01-30 Brentford – Wycombe 7:2 150.00 2021-01-26 Cliftonville – Linfield 4:3 150.00 2021-01-23 Wealdstone – Aldershot 3:4 150.00 2021-01-19 Avai – Juventude EC 5:2 150.00 2021-01-16 Lingby – Hellerup 5:2 150.00 2021-01-12 Unterhaching – Verl 3:4 150.00 2021-01-09 Guarnizo – Barreda Balompie 3:4 150.00 2021-01-05 Konyaspor – Galatasaray 4:3 150.00 2021-01-02 Helmond Sport – Dordrecht 5:2 150.00 2020-12-29 Burton – Wigan 3:4 150.00 2020-12-26 Torquay – Yeovil 6:1 150.00 2020-12-22 Floriana – Birkirkara 2:5 150.00 2020-12-19 Dag & Red – Ebbsfleet Utd 5:2 150.00 2020-12-15 Spennymoor – Marske 6:2 150.00 2020-12-12 Charlton – AFC Wimbledon 5:2 150.00 2020-12-08 Milton Keynes – Norwich U21 6:0 150.00 2020-12-05 Bolton – Port Vale 3:6 150.00 2020-11-28 Elgin – Cowdenbeath 5:2 150.00 2020-11-21 Stoke – Huddersfield 4:3 150.00 2020-11-17 Dover – Woking 1:5 150.00 2020-11-10 Universitario D – Cajamarca 1:6 150.00 2020-11-07 Lincoln City – Forest Green 6:2 150.00 2020-11-03 Salzburg – Bayern München 2:6 150.00 2020-10-31 Vicenza – Pisa 4:4 150.00 2020-10-27 Hednesford – Bromsgrove 5:1 150.00 2020-10-24 Queen of South – Raith Rovers 2:5 150.00 2020-10-20 Kriens SC – Grasshoppers 5:2 150.00 2020-10-17 Hamilton – St. Johnstone 3:5 150.00 2020-10-13 Sholing – Walton Casuals 5:2 150.00 2020-10-10 St. Johnstone – Brechin 7:0 150.00 2020-10-06 Sunderland – Carlisle 5:3 150.00 2020-10-03 Moneyfields – Cray Wanderers 2:6 150.00 2020-09-29 Blumenthaler SV – Geestemunde 6:1 150.00 2020-09-26 Shamrock II – UCD 2:5 150.00 2020-09-22 Birmingham U23 – Crewe U23 4:5 150.00 2020-09-19 Leeds Utd – Fulham 4:3 150.00 2020-09-15 Tshakhuma – Ajax Cape Town 1:5 150.00 2020-09-12 MyPa – VPS 3:6 150.00 2020-09-08 Young Boys – Lausanne 5:2 150.00 2020-09-05 West Ham Utd – Bournemouth 3:5 150.00 2020-09-01 Drassburg – Leobendorf 3:4 150.00 2020-08-29 FK Minsk – Nema 4:3 150.00 2020-08-25 Septemvri Sofia – Lokomotiv Sofia 2:5 150.00 2020-08-22 Vejle – Viborg 3:4 150.00 2020-08-18 Chikhura Sachkhere – Saburtalo Tbilisi 2:8 150.00 2020-08-15 Lyngby – Hellerup 4:3 150.00 2020-08-11 Bnei Yehuda – Ashdod 1:5 150.00 2020-08-08 Ajax – RKC Waalwijk 6:1 150.00 2020-08-01 Kozakken Boys – Sparta Nijkerk 3:5 150.00 2020-07-28 Foutoua – Al Jaish (SYR) 1:6 150.00 2020-07-21 Boston Utd – Gateshead 5:3 150.00 2020-07-21 FC Hellas Kagran – Stripfing 0:8 150.00 2020-07-18 Fulham – Sheffield Wed. 5:3 150.00 2020-07-14 Wigan – Hull 8:0 150.00 2020-07-11 Fram – Leiknir Reykjavik 2:5 150.00 2020-07-07 Alashkert – Lori 5:1 150.00 2020-07-04 Winterthur – Aarau 4:3 150.00 2020-06-30 Winterthur – Aarau 4:2 150.00 2020-06-27 Trans Narva – Levadia Talinn 4:4 150.00 2020-06-24 Inter T. – KuPS 4:4 150.00 2020-06-20 Lida – Lokomotiv Gomel 4:2 150.00 2020-06-16 Wehen – Nurnberg 0:6 150.00 2020-06-13 FK Gomel – Lida 7:2 150.00 2020-06-10 Municipal Grecia – San Carlos 2:5 150.00 2020-06-06 Energetik-Bgatu – FC Ostrovets 2:6 150.00 2020-06-02 Union Plaani – Nopsa 2:5 150.00 2020-05-30 Plzen – Mlada Boleslav 7:1 150.00 2020-05-26 Blansko – SFK Vrchovina 3:4 150.00 2020-05-23 Viktorie Prerov – Nove Sady 2:5 100.00 2020-05-16 An Giang – Dong Thap 3:3 100.00 2020-05-09 Neman – Rukh Brest 2:4 100.00 2020-04-25 Shakhter Petrikov – Victoria Maryina Gorka 5:2 100.00 2020-04-04 Dinamo Brest – Slavia-Mozyr 1:2 25.00 2020-03-28 FK Gomel – Ivatsevichi 8:1 151.00 2020-03-22 Vingakers IF – Ericsbergs GOIF 2:7 151.00 2020-03-21 Hacken – JonkopingsSodra 6:1 151.00 2020-03-14 Zenit – Ural 7:1 151.00 2020-03-07 Dungannon – Warrenpoint Town 4:4 151.00 2020-03-03 Chesterfield – Harrogate Town 3:4 26.00 2020-02-29 Oldham – Newport County 5:0 151.00 2020-02-25 Hednesford T – Peterborough S 3:3 23.00 2020-02-22 Senglea Athletic – St. Lucia 2:5 151.00 2020-02-15 Accrington – Lincoln City 4:3 101.00 2020-02-11 Colchester – Grimsby 2:3 51.00 2020-02-08 Lewes – Cheshunt 1:6 150.00 2020-02-04 Altrincham – AFC Telford 5:2 150.00 2020-02-01 Montrose – Peterhead 4:3 150.00 2020-01-28 Alfreton – Gloucester City 6:1 150.00 2020-01-25 Raith Rovers – Montrose 4:3 150.00 2020-01-21 Hrv. Dragovoljac – Vinogradar 2:4 150.00 2020-01-18 Jaro 11.0 – Seinajoen JK 2:5 150.00 2020-01-14 00:00:00 Esbjerg – Kolding IF 3:4 150.00 2020-01-12 00:00:00 Moratalaz – Leganes (B) 3:4 150.00 2020-01-07 00:00:00 Ingolstadt – St. Gallen 6:3 150.00 2020-01-04 00:00:00 Barrow – Ebbsfleet Utd 7:0 150.00 2020-01-01 00:00:00 Rhyl – Llanrhaedr 3:5 150.00 2019-12-28 00:00:00 Academica – Oliveirense 4:3 150.00 2019-12-21 00:00:00 Kasimpasa – Gazisehir Gaziantep 3:4 150.00 2019-12-17 00:00:00 Twente – Go Ahead Eagles 2:5 150.00 2019-12-14 00:00:00 Barnsley – Queen’s Park R. 5:3 150.00 2019-12-10 00:00:00 Ittihad Kalba – Ajman 6:2 150.00 2019-12-07 00:00:00 Akhisar Bel. – Istanbulspor 4:3 150.00 2019-12-03 00:00:00 Beroe – CSKA 1948 Sofia 3:4 150.00 2019-11-30 00:00:00 Brentford – Luton 7:0 150.00 2019-11-27 00:00:00 Buducnost – Jedinstvo 7:0 150.00 2019-11-23 00:00:00 Enfield Town – Maldon & Tiptree 4:3 150.00 2019-11-19 00:00:00 Niger – Madagascar 2:6 150.00 2019-11-16 00:00:00 Bromley – Harrogate Town 3:3 150.00 2019-11-13 00:00:00 Derby County U23 – Leeds U23 7:1 150.00 2019-11-09 00:00:00 Queen’s Park – Brechin 5:2 150.00 2019-11-05 00:00:00 Merthyr – Cinderford Town 4:4 150.00 2019-11-02 00:00:00 Blackpool – Peterborough 4:3 150.00 2019-10-30 00:00:00 Warriors FC – Brunei DPMM 4:3 150.00 2019-10-26 00:00:00 Torquay – Barrow 4:2 150.00 2019-10-22 00:00:00 Billericay – Sutton Utd 5:2 150.00 2019-10-19 00:00:00 Clachnacuddin – Brora Rangers 0:7 150.00 2019-10-15 00:00:00 Leiston FC – St Ives Town 4:4 150.00 2019-10-12 00:00:00 Mansfield – Oldham 6:1 150.00 2019-10-08 00:00:00 Cheltenham Town – West Ham U21 4:3 150.00 2019-10-05 00:00:00 Hayes & Yeading – Hendon 5:4 150.00 2019-10-01 00:00:00 Jong Den Bosch – Jong Excelsior 4:2 150.00 2019-09-28 00:00:00 Crusaders – Glentoran 5:2 150.00 2019-09-24 00:00:00 Waasland-Beveren – Westerlo 3:4 150.00 2019-09-21 00:00:00 Dulwich Hamlet – Bognor Regis T 6:1 150.00 2019-09-18 00:00:00 FH – IBV 6:4 150.00 2019-09-14 00:00:00 Peterborough – Rochdale 6:0 150.00 2019-09-10 00:00:00 Varde – Thisted 3:4 150.00 2019-09-07 00:00:00 Dartford – Dorking Wanderers 3:4 150.00 2019-09-03 00:00:00 AFC Telford – Gloucester City 4:3 150.00 2019-08-31 00:00:00 Grorud IL – Asane 4:3 150.00 2019-08-27 00:00:00 Flint – Conwy Borough 5:3 150.00 2019-08-25 00:00:00 Buxton – Whitby 3:4 150.00 2019-08-20 00:00:00 Salzburg (Aut) – BW Linz (Aut) 7:1 150.00 2019-08-17 00:00:00 Asker – Asane 4:3 150.00 2019-08-13 00:00:00 Chorley – Solihull 1:6 150.00 2019-08-10 00:00:00 Raith Rovers – Clyd 5:2 150.00 2019-08-06 00:00:00 Habenhauser – Hemelingen 4:5 150.00 2019-08-03 00:00:00 BATE Borisov – FK Tarpeda Minsk 8:1 150.00 2019-07-31 00:00:00 Ratchaburi – Samut Prakan 5:2 150.00 2019-07-27 00:00:00 Unterhaching – Würzburger Kickers 5:4 150.00 2019-07-23 00:00:00 Mannsworth – Stadlau 3:5 150.00 2019-07-20 00:00:00 Eichstätt – Schalding-Heining 7:0 150.00 2019-07-16 00:00:00 Hampton & Richmond – Chesham 1:5 150.00 2019-07-13 00:00:00 Liefering – Brighton 2:5 150.00 2019-07-09 00:00:00 Wiener SK – Horn 4:3 150.00 2019-07-06 00:00:00 Oud Heverlee – Mechelen 4:4 150.00 2019-07-03 00:00:00 Midtjylland – Vitesse 4:3 150.00. CONTACT. Need to get in touch? Shall you have any queries or requests, which are not answered by the information provided on our website. Interested in working with us, or have a question? Feel free to contact us anytime. WHY CHOOSE US. As a team in betting community worldwide, we are dedicated to provide you correct, valid and profitable information. Our purpose is only your maximum profit through professional betting based on strategy and particular gainful methods. WHEN CAN I PURCHASE PICKS? You can purchase picks at any time 24 hours a day, 7 days a week. HOW MUCH SHOULD I BET? You can just test us out with a moderate amount during the first couple of games, but later on you can slowly increase the amount you bet. But obviously it’s all up to you and depends on the risk appetite that you have. WHAT HAPPENS IF THE MATCH FAILS? In this case, you will get the closest match for free. 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