# Predicting Fantasy Football Performance With Machine .

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Nitin KapaniaCS 229 Final ProjectPredicting Fantasy Football Performance with Machine Learning TechniquesIntroduction and BackgroundOnce a paper and pencil game played only by a few sports aficionados, the internet has helped transformfantasy sports into a 1 billion dollar industry. Accounting for nearly 40% of this industry is football, withmillions of casual fans playing in fantasy football leagues every year.The basic premise of fantasy football is as follows. A fantasy football league, typically consisting of 8-10competitors, holds a “draft” before every NFL season where each fantasy competitor has a limitednumber of virtual resources (usually a salary cap or a fixed number of draft picks) available to spend.Using these resources, each competitor selects a virtual team comprised of real NFL athletes. Fantasycompetitors then face one another in heads-up games every week of the NFL season, with scoring in thefantasy games dictated by the statistical in-game performance (i.e. yards gained, touchdowns scored, etc.)of the NFL athletes in their actual games.The major challenge of fantasy football is therefore to select players who provide good statisticalperformance relative to their price in the draft. As an avid fantasy football player, I decided to focus myfinal project on building statistical models to predict the NFL athletes who will score the most fantasypoints in a given season.Project ScopeIn general, fantasy teams consist of at least one quarterback, two wide receivers, two running backs, afield goal kicker, and a tight end. To limit the scope of the project, this project will generate pre-seasonpredictions for running backs (RBs) only. However, results from this project can be generalized todevelop models for all other NFL positions as well.Fantasy Point Rules for Running BacksFantasy point scoring for a running back in a given week is given by the following two simple rules:First Crack at the Problem – Using Linear RegressionMy first project goal was to get a very simple learning model up and running. Given that the number offantasy points scored by a running back can be viewed as a continuous output, I decided to start with asimple linear regression model with only two features1:1I chose to normalize my feature vectors by the number of games a running back played in a given season, to avoid penalizingrunning backs who missed games due to injury/suspension/contract disputes, etc.

Nitin KapaniaCS 229 Final ProjectThe model therefore predicts fantasy point scoring for a running back solely on how many yards andtouchdowns they had in the previous year. This is admittedly a simple choice of a feature vector, but sincefantasy point scoring is exclusively dependent on scoring touchdowns and gaining yards, it makes senseto start with this choice of feature vector as a baseline.Data Collection:A training set was collected from the statistics of m 34 running backs finishing with at least 70 fantasypoints in both the 2007 and 2008 NFL seasons. The yardage and touchdown statistics to form the featuredata x were collected from 2007, and the fantasy point totals for the target variable y were collected from2008.2Results of Linear Regression:To test the regression model, I made predictions of how 32 running backs would perform in the 2010NFL season, based on their performance in the 2009 NFL season. Figure 1 shows a learning curve for theregression algorithm. The error metric on the y axis is the average estimation error between predicted andactual running back performance, given in fantasy points/year. The figure shows that the estimation errorstays roughly constant after m 15, and that our average estimation error is slightly higher, but on parwith the predictions of Mike Krueger, a human expert who makes fantasy predictions for fftoday.com.Figure 1: Learning curve for linear regression algorithm. Note that training and test error are similar asnumber of training samples increases.2The 70 point cutoff for the training set was chosen to exclude running backs whose first season in the NFL was in2010, as well as running backs that missed significant time due to injury in either season. Data was collected fromfftoday.com

Nitin KapaniaCS 229 Final ProjectWhile a reasonable metric to evaluate a learning curve, “average prediction error” as defined above is notthe best metric for comparing two prediction methods, since winning in fantasy football is about relativeperformance between running backs. A better way is to evaluate the algorithm is to use the numericalpredictions to create a ranked list of running backs for the upcoming season, and then see how these picksactually end up performing in 2010. This is shown in Table 1.LinearRegressionChris JohnsonAdrian PetersonMaurice JonesDrewFrank GoreRay RiceThomas JonesSteven JacksonCedric BensonMichael TurnerRicky WilliamsPredicting the Top 10 Running Backs of 2010PredictedHuman ExpertPredictedActual 2010Points(Mike Krueger)PointsRankings242Adrian Peterson283Arian Foster241Chris Johnson277Peyton Hillis233Maurice Jones270Adrian PetersonDrew222Ray Rice246Jamaal Charles221Frank Gore224Chris Johnson217Ryan Mathews220Darren 3Steven Jackson215LeSean McCoy206Michael Turner211Michael Turner202DeAngelo210Matt ForteWilliamsActual Points329243231231232226222222217215Table 1: Running back predictions compared to actual results. First column is from my linear regressionalgorithm, second column is from a human expert, third column is actual results. Values in parenthesisrepresent predicted/actual points scored. Rankings accurate to within five positions are shown in green.Questionable picks are shown in red.The difficulty of predicting fantasy performance is immediately apparent. Very few people predicted theexplosive emergence of Jamaal Charles, LeSean Mccoy, andAdrian Foster, who were two youngnewcomers to the NFL in 2010. Similarly, the injury of Maurice Jones-Drew, one of the NFLs mostconsistent running backs, shook up the final season rankings further. A second observation is the relativesimilarity between Mike Krueger’s predictions and the predictions from linear regression. The two sets ofpredictions share seven common players, each ranked within 1-2 spots of one another.Another interesting observation is the regression algorithm’s high ranking of Thomas Jones and RickyWilliams. While both athletes had solid 2009 seasons, both players were moved to backup roles beforethe 2010 season as they competed for playing time with younger running backs on their teams. Mostfantasy football experts, Mike Krueger included, therefore had these two ranked well outside the top 30,as it was unlikely they would repeat their 2009 performance. Without a way to capture this preseasoninformation, the algorithm as presented is unable to recognize the risk associated with these two players.A Second Attempt at the Problem – Using a Clustering AlgorithmAn alternate approach to predicting good fantasy football players is to group NFL running backs intoseveral clusters, based on a variety of features such as number of games played, number of rushingattempts, rushing yards, touchdowns, and total fantasy points scored. Player predictions are then made byfirst classifying running backs into their corresponding group, and then applying a regression modelunique to that group.

Nitin KapaniaCS 229 Final ProjectFigure 2: Combination of clustering and linear regression algorithm used to make predictionsThe idea behind this method is that there may be several fundamental types of running backs in the NFL.In this case, it’s possible to get more accurate predictions by having a different set of linear regressioncoefficients for each type of player. For example, players who were injured in one season will have anartificially low number of fantasy points scored that year, and will often see a dramatic increase in fantasypoints the next year simply by being healthy. This cluster might therefore have relatively larger regressioncoefficients compared to a cluster of players who stayed healthy.To perform the k-means clustering, I gathered a larger dataset of training data, encompassing thestatistical performance of m 292 running backs from 2006 to 2008. After experimenting with a numberof feature combinations, I found it best to cluster the running backs using only three features: number ofgames played, total yards per game, and total touchdowns per game.50Average Prediction Error(Fantasy Points/season)49484746454443424112345678910Number of Clusters (k)Figure 3: Player prediction error as a function of clusters usedTo determine the number of clusters to use, I calculated the average prediction error (the same metricused for linear regression) for a variety of k (see Figure 3).I found that in terms of this metric, the number of clusters to use wasn’t immediately obvious, as theprediction error hovered around 42 – 45 points per year for k 1 to 6. However, I found that as thenumber of clusters increased beyond six, the clustering algorithm tended to get stuck in local minima andcame up with increasingly erroneous predictions. In terms of qualitative performance, I found that themachine learning algorithm came up with the most reasonable picks at k 3 or 4.

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