Senior Project – Computer Science Machine Learning in Football Andrew Finley Advisor – Prof. Striegnitz Research Question: Every year there are players who move from collegiate football to professional football with high expectations and never meet them. Likewise, there are players with low expectations who exceed them. This leads me to question, is it possible to accurately predict the success of NFL players based on their collegiate performance? A player is generally considered successful if he is starting a majority of his games by his third season. The goal of this project is to build a program that will predict a player’s professional statistics, given their collegiate statistics. For the sake of time, I am only looking at quarterbacks and running backs. PlayerSchoolYear1Pos1Cl1G1Rush Yds1Car1Rush TD1Yds/Car1RushYds/G1Rec Yds1Rec1Rec TD1Yds/Rec1Rec/G1RecYds/G1PR1PR Yds1PR TD1Yds/PR1PR/G1KR1KR Yds1KR TD1Yds/KR1KR/G1Ret TD1Tot Yds1Tot TD1TotYds/G1 Ronnie BrownAuburn2002RBSo Year2Pos2Cl2G2Rush Yds2Car2Rush TD2Yds/Car2RushYds/G2Rec Yds2Rec2Rec TD2Yds/Rec2Rec/G2RecYds/G2PR2PR Yds2PR TD2Yds/PR2PR/G2KR2KR Yds2KR TD2Yds/KR2KR/G2Ret TD2Tot Yds2Tot TD2TotYds/G2 2003RBJr Year3Pos3Cl3G3Rush Yds3Car3Rush TD3Yds/Car3RushYds/G3Rec Yds3Rec3Rec TD3Yds/Rec3Rec/G3RecYds/G3PR3PR Yds3PR TD3Yds/PR3PR/G3KR3KR Yds3KR TD3Yds/KR3KR/G3Ret TD3Tot Yds3Tot TD3TotYds/G3 2004RBSr HeightWeight 6'-1''230 Season1Team1G1GS1Att1RushYds1RushAvg1RushLng1RushTD1Rec1RecYds1RecAvg1RecLng1RecTD1FUM1Lost1Starting 2005MiamiDolphins TRUE Season2Team2G2GS2Att2RushYds2RushAvg2RushLng2RushTD2Rec2RecYds2RecAvg2RecLng2RecTD2FUM2Lost2Starting 2006MiamiDolphins TRUE Season3Team3G3GS3Att3RushYds3RushAvg3RushLng3RushTD3Rec3RecYds3RecAvg3RecLng3RecTD3FUM3Lost3Starting 2007MiamiDolphins TRUE Data: Step 1: Gather data by parsing it off websites (NFL.com, NCAA.org) with Python scripts, and through Collegio Football (database program). Step 2: Use more Python scripts to combine data into two large.csv files for quarterbacks and running backs Step 3: Fix any left over formatting errors, and fill in any missing statistics possible. Step 4: Input into Weka (ML software), and predict desired statistics Step 5: Evaluate accuracy using cross validation Preliminary Results: Difficulty building trees with large sets of training data, better trees made when attributes are selected by hand. Baseline for accuracy is 68%, this is given if all predictions for “starting third season” are set to false and no tree is constructed Accuracy of the program varies significantly with different feature sets, feature selection is very important Classification using Decision Trees: The idea behind this project is to use classification algorithms to train a program to predict NFL stats when given collegiate stats. Classification is the process of training a program on a set of known instances, to predict unknown ones. I am using a Decision Tree algorithm to train the program. A decision tree algorithm: Creates a graph (tree) from the training data. The leaves are the classes, and branches are attribute values Goal is to make the smallest tree possible that covers all instances Uses the tree to make a set of classification rules. Next Step: Continue with different feature selections to improve accuracy to beat baseline - Sample input for running back data, blue are inputs, red are possible outputs