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
The model is founded on the number of goals scored/conceded by each team. Teams that have been higher scorers in the past have a greater likelihood of scoring goals in the future. We’ll import all match results from the recently concluded Premier League (2016/17) season. There’s various sources for this data out there (kaggle, football-data.co.uk, github, API). I built an R wrapper for that API, but I’ll go the csv route this time around.
import pandas as pdimport matplotlib.pyplot as pltimport numpy as npimport seabornfrom scipy.stats import poisson,skellamepl_1617 = pd.read_csv("http://www.football-data.co.uk/mmz4281/1617/E0.csv")epl_1617 = epl_1617[['HomeTeam','AwayTeam','FTHG','FTAG']]epl_1617 = epl_1617.rename(columns={'FTHG': 'HomeGoals', 'FTAG': 'AwayGoals'})epl_1617.head()| 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).
epl_1617 = epl_1617[:-10]epl_1617.mean()HomeGoals 1.591892AwayGoals 1.183784dtype: float64You’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.
# probability of draw between home and away teamskellam.pmf(0.0, epl_1617.mean()[0], epl_1617.mean()[1])0.24809376810717076# probability of home team winning by one goalskellam.pmf(1, epl_1617.mean()[0], epl_1617.mean()[1])0.22558259663675409
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?).
# importing the tools required for the Poisson regression modelimport statsmodels.api as smimport statsmodels.formula.api as smfgoal_model_data = pd.concat([epl_1617[['HomeTeam','AwayTeam','HomeGoals']].assign(home=1).rename( columns={'HomeTeam':'team', 'AwayTeam':'opponent','HomeGoals':'goals'}), epl_1617[['AwayTeam','HomeTeam','AwayGoals']].assign(home=0).rename( columns={'AwayTeam':'team', 'HomeTeam':'opponent','AwayGoals':'goals'})])poisson_model = smf.glm(formula="goals ~ home + team + opponent", data=goal_model_data, family=sm.families.Poisson()).fit()poisson_model.summary()| 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.
poisson_model.predict(pd.DataFrame(data={'team': 'Chelsea', 'opponent': 'Sunderland', 'home':1},index=[1]))array([ 3.06166192])poisson_model.predict(pd.DataFrame(data={'team': 'Sunderland', 'opponent': 'Chelsea', 'home':0},index=[1]))array([ 0.40937279])Just like before, we have two Poisson distributions. From this, we can calculate the probability of various events. I’ll wrap this in a simulate_match function.
def simulate_match(foot_model, homeTeam, awayTeam, max_goals=10): home_goals_avg = foot_model.predict(pd.DataFrame(data={'team': homeTeam, 'opponent': awayTeam,'home':1}, index=[1])).values[0] away_goals_avg = foot_model.predict(pd.DataFrame(data={'team': awayTeam, 'opponent': homeTeam,'home':0}, index=[1])).values[0] team_pred = [[poisson.pmf(i, team_avg) for i in range(0, max_goals+1)] for team_avg in [home_goals_avg, away_goals_avg]] return(np.outer(np.array(team_pred[0]), np.array(team_pred[1])))simulate_match(poisson_model, 'Chelsea', 'Sunderland', max_goals=3)array([[ 0.03108485, 0.01272529, 0.00260469, 0.00035543], [ 0.0951713 , 0.03896054, 0.00797469, 0.00108821], [ 0.14569118, 0.059642 , 0.01220791, 0.00166586], [ 0.14868571, 0.06086788, 0.01245883, 0.0017001 ]])This matrix simply shows the probability of Chelsea (rows of the matrix) and Sunderland (matrix columns) scoring a specific number of goals. For example, along the diagonal, both teams score the same the number of goals (e.g. P(0-0)=0.031). So, you can calculate the odds of draw by summing all the diagonal entries. Everything below the diagonal represents a Chelsea victory (e.g P(3-0)=0.149). If you prefer Over/Under markets, you can estimate P(Under 2.5 goals) by summing the entries where the sum of the column number and row number (both starting at zero) is less than 3 (i.e. the 6 values that form the upper left triangle). Luckily, we can use basic matrix manipulation functions to perform these calculations.
chel_sun = simulate_match(poisson_model, "Chelsea", "Sunderland", max_goals=10)# chelsea winnp.sum(np.tril(chel_sun, -1))0.8885986612364134# drawnp.sum(np.diag(chel_sun))0.084093492686495977# sunderland winnp.sum(np.triu(chel_sun, 1))0.026961819942853051Hmm, our model gives Sunderland a 2.7% chance of winning. But is that right? To assess the accuracy of the predictions, we’ll compare the probabilities returned by our model against the odds offered by the Betfair exchange.
Sports Betting/Trading
Unlike traditional bookmakers, on betting exchanges (and Betfair isn’t the only one- it’s just the biggest), you bet against other people (with Betfair taking a commission on winnings). It acts as a sort of stock market for sports events. And, like a stock market, due to the efficient market hypothesis, the prices available at Betfair reflect the true price/odds of those events happening (in theory anyway). Below, I’ve posted a screenshot of the Betfair exchange on Sunday 21st May (a few hours before those matches started).
The numbers inside the boxes represent the best available prices and the amount available at those prices. The blue boxes signify back bets (i.e. betting that an event will happen- going long using stock market terminology), while the pink boxes represent lay bets (i.e. betting that something won’t happen- i.e. shorting). For example, if we were to bet £100 on Chelsea to win, we would receive the original amount plus 100*1.13= £13 should they win (of course, we would lose our £100 if they didn’t win). Now, how can we compare these prices to the probabilities returned by our model? Well, decimal odds can be converted to the probabilities quite easily: it’s simply the inverse of the decimal odds. For example, the implied probability of Chelsea winning is 1/1.13 (=0.885- our model put the probability at 0.889). I’m focusing on decimal odds, but you might also be familiar with Moneyline (American) Odds (e.g. +200) and fractional odds (e.g. 2/1). The relationship between decimal odds, moneyline and probability is illustrated in the table below. I’ll stick with decimal odds because the alternatives are either unfamiliar to me (Moneyline) or just stupid (fractional odds).
| Match | Home | Draw | Away |
|---|---|---|---|
| Arsenal v Everton | 71.4 % | 17.5 % | 11.6 % |
| Burnley v West Ham | 42 % | 27.8 % | 30.8 % |
| Chelsea v Sunderland | 88.5 % | 8.7 % | 3.4 % |
| Hull v Tottenham | 10.9 % | 17.2 % | 71.9 % |
| Leicester v Bournemouth | 53.5 % | 24.4 % | 23.3 % |
| Liverpool v Middlesbrough | 87.7 % | 9.5 % | 3.6 % |
| Man Utd v C Palace | 41.7 % | 29 % | 29.9 % |
| Southampton v Stoke | 57.1 % | 24.4 % | 19.2 % |
| Swansea v West Brom | 43.1 % | 28.6 % | 29 % |
| Watford v Man City | 5.1 % | 10.2 % | 85.5 % |
So, we have our model probabilities and (if we trust the exchange) we know the true probabilities of each event happening. Ideally, our model would identify situations the market has underestimated the chances of an event occurring (or not occurring in the case of lay bets). For example, in a simple coin toss game, imagine if you were offered $2 for every $1 wagered (plus your stake), if you guessed correctly. The implied probability is 0.333, but any valid model would return a probability of 0.5. The odds returned by our model and the Betfair exchange are compared in the table below.
| Match | Home | Draw | Away | |
|---|---|---|---|---|
| Arsenal v Everton | Betfair | 0.714 | 0.175 | 0.116 |
| Predicted | 0.533 | 0.226 | 0.241 | |
| Difference | 0.181 | -0.051 | -0.125 | |
| Burnley v West Ham | Betfair | 0.42 | 0.278 | 0.308 |
| Predicted | 0.461 | 0.263 | 0.276 | |
| Difference | -0.041 | 0.015 | 0.032 | |
| Chelsea v Sunderland | Betfair | 0.885 | 0.087 | 0.034 |
| Predicted | 0.889 | 0.084 | 0.027 | |
| Difference | -0.004 | 0.003 | 0.007 | |
| Hull v Tottenham | Betfair | 0.109 | 0.172 | 0.719 |
| Predicted | 0.063 | 0.138 | 0.799 | |
| Difference | 0.046 | 0.034 | -0.08 | |
| Leicester v Bournemouth | Betfair | 0.535 | 0.244 | 0.233 |
| Predicted | 0.475 | 0.22 | 0.306 | |
| Difference | 0.06 | 0.024 | -0.073 | |
| Liverpool v Middlesbrough | Betfair | 0.877 | 0.095 | 0.036 |
| Predicted | 0.77 | 0.161 | 0.069 | |
| Difference | 0.107 | -0.066 | -0.033 | |
| Man Utd v C Palace | Betfair | 0.417 | 0.29 | 0.299 |
| Predicted | 0.672 | 0.209 | 0.119 | |
| Difference | -0.255 | 0.081 | 0.18 | |
| Southampton v Stoke | Betfair | 0.571 | 0.244 | 0.192 |
| Predicted | 0.496 | 0.277 | 0.226 | |
| Difference | 0.075 | -0.033 | -0.034 | |
| Swansea v West Brom | Betfair | 0.431 | 0.286 | 0.29 |
| Predicted | 0.368 | 0.266 | 0.366 | |
| Difference | 0.063 | 0.02 | -0.076 | |
| Watford v Man City | Betfair | 0.051 | 0.102 | 0.855 |
| Predicted | 0.167 | 0.203 | 0.631 | |
| Difference | -0.116 | -0.101 | 0.224 | |
Green cells illustrate opportunities to make profitable bets, according to our model (the opacity of the cell is determined by the implied difference). I’ve highlighted the difference between the model and Betfair in absolute terms (the relative difference may be more relevant for any trading strategy). Transparent cells indicate situations where the exchange and our model are in broad agreement. Strong colours imply that either our model is wrong or the exchange is wrong. Given the simplicity of our model, I’d lean towards the latter.
Something’s Poissony
So should we bet the house on Manchester United? Probably not (though they did win!). There’s some non-statistical reasons to resist backing them. Keen football fans would notice that these matches represent the final gameweek of the season. Most teams have very little to play for, meaning that the matches are less predictable (especially when they involve unmotivated ‘bigger’ teams). Compounding that, Man United were set to play Ajax in the Europa Final three days later. Man United manager, Jose Mourinho, had even confirmed that he would rest the first team, saving them for the much more important final. In a similar fashion, injuries/suspensions to key players, managerial sackings would render our model inaccurate. Never underestimate the importance of domain knowledge in statistical modelling/machine learning! We could also think of improvements to the model that would incorporate time when considering previous matches (i.e. more recent matches should be weighted more strongly).
Statistically speaking, is a Poisson distribution even appropriate? Our model was founded on the belief that the number goals can be accurately expressed as a Poisson distribution. If that assumption is misguided, then the model outputs will be unreliable. Given a Poisson distribution with mean , then the number of events in half that time period follows a Poisson distribution with mean /2. In football terms, according to our Poisson model, there should be an equal number of goals in the first and second halves. Unfortunately, that doesn’t appear to hold true.
epl_1617_halves = pd.read_csv("http://www.football-data.co.uk/mmz4281/1617/E0.csv")epl_1617_halves = epl_1617_halves[['FTHG', 'FTAG', 'HTHG', 'HTAG']]epl_1617_halves['FHgoals'] = epl_1617_halves['HTHG'] + epl_1617_halves['HTAG']epl_1617_halves['SHgoals'] = epl_1617_halves['FTHG'] + epl_1617_halves['FTAG'] - epl_1617_halves['FHgoals']epl_1617_halves = epl_1617_halves[['FHgoals', 'SHgoals']]
We have irrefutable evidence that violates a fundamental assumption of our model, rendering this whole post as pointless as Sunderland!!! Or we can build on our crude first attempt. Rather than a simple univariate Poisson model, we might have more success with a bivariate Poisson distriubtion. The Weibull distribution has also been proposed as a viable alternative. These might be topics for future blog posts.
Summary
We built a simple Poisson model to predict the results of English Premier League matches. Despite its inherent flaws, it recreates several features that would be a necessity for any predictive football model (home advantage, varying offensive strengths and opposition quality). In conclusion, don’t wager the rent money, but it’s a good starting point for more sophisticated realistic models. Thanks for reading!
FAQs
How do you predict football matches using statistics? ›
The most widely used statistical approach to prediction is ranking. Football ranking systems assign a rank to each team based on their past game results, so that the highest rank is assigned to the strongest team. The outcome of the match can be predicted by comparing the opponents' ranks.
What is the model for predicting football results? ›First developed in 1982, the double Poisson model, where goals scored by each team are assumed to be Poisson distributed with a mean depending on attacking and defensive strengths, remains a popular choice for predicting football scores, despite the multitude of newer methods that have been developed.
What is the best method for predicting football matches? ›One of the best ways to predict football matches is by using data and statistics. You can use data to find patterns in how teams play. For example, you can look at how a team has performed in the past against similar opponents.
Can AI predict football results? ›Kickoff.ai uses machine learning to predict the results of football matches. Based on data about national teams from the past, we model outcomes of football matches in order to predict future confrontations.
Who is the most accurate football predictor? ›PredictZ is hailed by many as the best and most reliable football prediction site in the world. They provide football tips, free analysis, football form and statistics, latest results, league tables, and many more. They cover all of the major football leagues but mostly focus on the English Premier League.
How is statistics used to predict? ›Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
What are the models for prediction? ›There are many different types of predictive modeling techniques including ANOVA, linear regression (ordinary least squares), logistic regression, ridge regression, time series, decision trees, neural networks, and many more.
Can we use linear regression to predict the winner of a football game? ›This intuitive idea of reversion to the mean is based on linear regression, a simple yet powerful data science method. It powers my preseason college football model that has predicted almost 70% of game winners the past 3 seasons.
What is the secret of predicting football? ›The most important information in predicting the Fair Lines are: Number of goals scored, Number of goals Conceded. This 2 informations are more powerful than the tables position, the number of points, the number of wins. The season's number are better predictors of Fair Lines than short-term information.
What is the most common method of scoring in football? ›In today's NFL, the most common way to score points in the NFL is the passing touchdown. This trend has also increased as the defense gains more chances to take advantage of the offense's mistakes. The second most common way of scoring is the field goal.
Which site can predict football matches correctly? ›
- BetEnsured.
- WindrawWin.
- PredictZ.
- Futbol24.
- Zulubet.
- Overlyzer.
- SoloPredict.
- 1960tips.
Paul the Octopus (26 January 2008 – 26 October 2010) was a common octopus who predicted the results of international association football matches. Accurate predictions in the 2010 World Cup brought him worldwide attention as an animal oracle.
How can AI predict sports outcomes? ›The use of AI-powered predictive analytics in sport betting
Machine learning algorithms are able to process large amounts of data quickly and accurately, allowing them to identify patterns that may not be obvious to the human eye. This can give bettors an edge when it comes to predicting the outcome of a game or event.
The NFL also works with the AWS Machine Learning Solutions Lab to take player safety to a new level. Using AI and ML on AWS, the NFL is building the Digital Athlete, a virtual representation of an NFL player that can be used to better predict and eventually prevent player injury.
What is the best football analysis site? ›Footballtipster.net can be your best football analysis partner. The site offers you the right information needed to strengthen your game against the bookies. You will find the best football tipsters here. Also, the results of the previous matches can help you draw the trend and use these for predictions later.
How accurate is Eagle predict? ›EaglePredict is the best football prediction site in the world with over 89.9% accuracy rate.
What are the three most used predictive modeling techniques? ›Three of the most widely used predictive modeling techniques are decision trees, regression and neural networks.
Which regression model is best for prediction? ›1) Linear Regression
It is one of the most-used regression algorithms in Machine Learning. A significant variable from the data set is chosen to predict the output variables (future values).
Regression and classification algorithms are the most popular options for predicting values, identifying similarities, and discovering unusual data patterns.
How do you create a prediction model in Excel? ›- Step 1 – Excel Options. Go to Files -> Options:
- Step 2 – Locate Analytics ToolPak. Go to Add-ins on the left panel -> Manage Excel Add-ins -> Go:
- Step 3 – Add Analytics ToolPak. Select the “Analysis ToolPak” and press OK:
What is performance prediction model? ›
In computer science, performance prediction means to estimate the execution time or other performance factors (such as cache misses) of a program on a given computer.
What are the two main predictive models? ›Regression and neural networks are two of the most widely used predictive modeling techniques. Companies can use predictive modeling to forecast events, customer behavior, and financial, economic, and market risks.
What machine learning model to use for prediction? ›The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics and our goal is to make the most accurate predictions possible.
What are the two types of predictive modeling? ›1. Simple linear regression: A statistical method to mention the relationship between two variables which are continuous. 2. Multiple linear regression: A statistical method to mention the relationship between more than two variables which are continuous.
What is the best regression model for sports? ›The base model that is most commonly used is logistic regression analysis. It is used to provide a probability percentage for a given variable and for sports it uses mv, or the margin of victory. This has always been the best indicator of a good team.
Is lasso better than linear regression? ›Therefore, lasso model is predicting better than both linear and ridge. Again lets change the value of alpha and see how does it affect the coefficients. So, we can see that even at small values of alpha, the magnitude of coefficients have reduced a lot.
Why linear regression is not suitable for prediction? ›Problem #1: Predicted value is continuous, not probabilistic
But in linear regression, we are predicting an absolute number, which can range outside 0 and 1. Using our linear regression model, anyone age 30 and greater than has a prediction of negative “purchased” value, which don't really make sense.
Tennis is one of the easiest sports to predict. For beginners, tennis is the best sport to predict the winner as there are no draws. It can be called a game sport. Tennis occurs very seldom, which makes it challenging for the predictors.
What statistics are used in football? ›Typical statistics include the average distance gained per each run, the percentage of passes caught, the average distance gained per successfully caught pass, average distance when kicking the ball to the other team, the number of times the ball is dropped when running with it (fumbles), and the number of times the ...
What is the least common method of scoring in American football? ›Safeties are the least common method of scoring in American football but are not rare occurrences – a safety has occurred around once every 14 games in the history of the National Football League (NFL), or about once a week under current scheduling rules.
What is the rarest NFL score ever? ›
The lowest possible score is 0-0 and has been achieved 73 times, though the most recent scoreless tie came on Nov. 7, 1943.
Which is the best app to Analyse football matches? ›- Soccer Predictions Football AI.
- Football Predictions.
- Tackl – football match prediction app with friends.
- Bullet Bet Predictions.
- Winner Expert.
- BetsWall Football Betting Tips.
- All Goals – Football Live Scores.
- BetMines.
The octopus made four accurate predictions, out of six, in the 2008 Euros. All were for games involving Germany. In the 2010 World Cup, Paul made eight predictions and all were accurate. The octopus chose Spain as the winner in the semifinal against Germany and La Furia Roja won the game 1-0.
How many games did Paul the octopus predict correctly? ›Paul, an octopus who lived in a German aquarium, correctly predicted eight World Cup games in 2010. He was found dead of natural causes later that fall.
What animal predicts the Super Bowl? ›Zoo animals also correctly picked the Chiefs and Eagles to win past Super Bowls. In 2020, a red panda from the zoo predicted the Chiefs would defeat the 49ers. Kansas City ended up winning that game 31-20. It's worth noting that the following year, a giraffe correctly pegged the Buccaneers to defeat the Chiefs.
How do you make a sports prediction model? ›- Choose Your Goal.
- Select Metrics/Data Points.
- Collect Said Data Points.
- Choose Type of Model.
- Build Your Model.
- Test Your Model.
- Start Cashing!
With the implementation of AI in football and other sports, coaches know when a player is projected to get injured and they can decide what to do in the situation. This method also predicts when the player will be healthy enough to resume practicing and play at a high level.
How does machine learning predict football matches? ›- Web-scraping. Web-scraping is the method of extracting relevant data for huge chunks of data available on different websites on the internet. ...
- Data Pre-Processing. ...
- Implementing Prediction Models. ...
- Evaluating Results Using Metrics.
The Important Role of Data Analytics in the NFL
The tracking system gathers player data on their speed, distance, acceleration, and location on the field per time. The data is collected in easily readable charts. These reports are used to determine the players' performance by set metrics.
But there is some evidence that the game is safer today than 10 or especially 20 or 30 years ago. And it's partly because of how the league is using machine learning.
What technology is used in NFL? ›
The NFL has used radio-frequency identification (RFID) transmitters (tags) using a system developed by Zebra Technologies. The league has permitted transmitters (tags) to be placed in each player's shoulder pads since 2014.
How do sports teams use statistics? ›Data analysis helps sports entities evaluate the performance of their athletes and assess the recruitment necessary to improve the team performance. It also evaluates the strong and weak areas of their challenger, enabling coaches to make the right decision on their tactics.
What are football statistics? ›Play-by-Play Counting Stats are the simplest type of statistics: the numbers that come directly from the official NFL play-by-play. That starts with all the standard statistics, anything you can find on NFL.com: yards, carries, passes, receptions, touchdowns, sacks, interceptions, and fumbles.
How is statistics used in sports? ›Sports analytics is the process of plugging statistics into mathematical models to predict the outcome of a given play or game. Coaches rely on analytics to scout opponents and optimize play calls in games, while front offices use it to prioritize player development.
How do statistics work in sports? ›A Sports Statistician analyzes data related to sporting events, usually for major sports such as soccer, baseball, football, or basketball. Sports Statisticians help with recording live data, tracking player data, evaluating roster picks, and making predictions about winning outcomes.
What is the most important statistic in football? ›The most important stats in football are offensive yards per carry and offensive yards per attempt. Offensive starting positions are also very important, as they can determine the outcome of a game.
How football clubs use data analytics? ›Football clubs use these data to get a competitive advantage and improve their skill and game plan on the field. Data analytics and statistics can help football clubs to track player performance, and physical preparation and even analyze the opposing team.
How football uses data and analytics to improve team performance? ›Based on data-driven insights about each player's past performance and behavior, clubs can then use this information to make more informed decisions during transfer windows. This helps clubs sign better players who are more likely to succeed in their team, improving team performance.
What is the best site for football statistics? ›- #1 KickForm.com.
- #2 Sofascore.com.
- #3 Transfermarkt.co.uk.
- #4 SoccerSTATS.com.
- #5 WhoScored.com.
- #6 Flashscore.com.
- #7 Soccerbase.com.
- #8 Footstats.co.uk.
GSIS is used league-wide to capture play and statistical information from every NFL stadium. GSIS produces the NFL's quarterly, half-time and game book reports distributed in the stadium. Information from GSIS also appears on television monitors and scoreboards in many NFL stadiums.
What is an example of statistics in sports? ›
For example, statistics can tell us how many goals a player has scored, or how many tackles they have made. They can also tell us which players are most successful when playing against certain teams, or in certain stadiums.
What does a football statistician do? ›Sports statisticians analyze data pertaining to sporting events, usually for major sports such as baseball, football or basketball. Sometimes called a scorer, sports statisticians record data live as it occurs.
What type of data is sports statistics? ›Sports analytics involves collecting and analyzing relevant historical statistics that can provide a competitive edge to a team or individual. With more sporting teams pouring investment into data analytics, several sports and statistics enthusiasts are flocking towards a career as a sports analyst.
How much do NFL statisticians make? ›How much does a Sports Statistician make? As of Mar 5, 2023, the average annual pay for a Sports Statistician in the United States is $74,161 a year. Just in case you need a simple salary calculator, that works out to be approximately $35.65 an hour. This is the equivalent of $1,426/week or $6,180/month.
Why do statistics matter in sports? ›Statistics also plays a key role in ensur- ing the integrity of sports ranging from baseball and basketball to cycling and sumo wrestling. Sporting events can be tainted by many factors, such as tank- ing, discrimination, doping, and judging bias. Demonstrating such breaches can be extremely challenging, however.
What is advanced statistics in sports? ›The labels may vary from sport to sport, but the idea of advanced stats involves the data that isn't found inside each sport's traditional box score. Think about it as the science behind sports stats that allows you to make decisions based on specific circumstances.