Welcome to College Football Analytics Newsletter by me, CFBNumbers! College Football through the lens of the computer. Data driven analysis of the game we all love!
For those of you that knew me on twitter as @NolesAnalytics, you’ll already know what kind of content and graphs I post about college football. For those of you that did not follow me or are just curious as to what you will get with this newsletter, allow me to give you an idea of what you’re signing up for.
The football analytics bread and butter is a metric called Expected Points Added (EPA). A quick explanation of expected points: how many points the team with the ball is expected to score given the game situation (quarter, time left, down, yards to go, field positioning etc.). It allows us to go beyond yards by adding context to each play. For a more detailed methodology I will link some papers down below.
EPA is just one of the many advanced metrics I will use to analyze team and player efficiency, as well as in game decision making. If you have ever listened to the commentary given in a football broadcast, or if you have perused social media after a 4th down decision gone awry, you’ve probably seen “analytics” used a some sort of a curse word. I promise you, us analytics enthusiasts are not trying to perform a hostile takeover of football! We don’t want to eliminate the RB position and force teams to only go for it on 4th down. We just want teams to use data driven strategies in order to put out the best on field product possible. If the data tells you to pass more, start slinging it! If it tells you its best to kick a FG here, ill take my 3 points please!
In addition to in game analysis, I am also interested in the other two aspects of college football: recruiting and the draft. Since this is launching in the offseason expect a heavier dose of recruiting and draft analysis to start. Here is just one example of some of the stuff to expect in terms of recruiting analysis. You should have seen the look of shock on my face when I saw my model told me Alabama was absurdly good at recruiting!
Last thing for now: I don’t want to just spit out some numbers and leave you at that. I believe analytics are extremely useful for a football team, but it should be used in conjunction with film study. Both are tools that have their advantages and disadvantages, and a healthy mix of the two is the optimal way to run a football program. So, when applicable, I will attempt to incorporate some film into my analysis in order to get a better understanding of the situation. Data can tell us what plays are more efficiently ran than others, or can point out overall weak spots in a team. Film can help us understand why certain schemes or techniques are more effective than others. I don’t have any examples of film study, so I will leave you with the most visually pleasing play in football. Fake RB toss, LB bites on the run, hit the TE up the seam. All done effortlessly by Trevor Lawrence:
Expected Points Added Links:
https://www.tomahawknation.com/2020/4/8/21188807/series-on-sports-analytics-college-football-expected-points-model-fundamentals-part-1 (This is just part 1 of a multi part series!)
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