I enjoy visualizations. Putting numbers to what we see happen in our day-to-day lives is helps me understand things better. Adding the context behind the numbers — I think — helps drive home a point.
I want to show Illinois through numbers for each game this year. It's not just the stats but a deeper analysis of what made Illinois win or lose the game. For each game this year, I will dive into the qualitative portion of the stats, but I want to explain the quantitative side for today. I want to share what these charts mean, and how I will interpret them moving forward.
So here we go.
Chart 1: Points + Cumulative EPA
As I explained last week, cumulative EPA is the expected points added each play of the game. A positive EPA means that the play will gain you points, a negative EPA means the play will take points away.
I chose to add all the EPA from each play to show performance over the 150-some plays throughout a game — both on offense and defense. I will differentiate this moving forward, but that may take a few weeks. If, throughout the game, the graph moves up, Illinois is always creating positive EPA plays on both sides of the ball. And if there is a negative trend, as a whole, those sets of plays were taking points away.
- Points are shown as dotted lines
- Points + Cumulative EPA are shows as a solid line
I hope I don't have to explain the color choice to you.
Choosing Points + Cumulative EPA allows me to visualize if and when Illinois is stagnating during the game, as Bret Bielema loooooooves to do. It is a great method to see if momentum is lost. See this game below:
Illinois went up by seven points in dramatic fashion — with some high EPA plays — and then all momentum was lost. Because Illinois went to a holding pattern, or a “let’s see if our defense can win this game for us” mentality, Michigan took advantage of Illinois, created some positive EPA plays, and won the game by 2 points. Had Illinois kept its momentum, this was a lock for Illinois to win. You cannot take your foot off the gas. Negative EPA plays won’t matter against bad teams, but they sure as hell do against good teams. See this comparison of Illinois vs. Northwestern and Michigan vs. Nebraska.
Good teams always create positive plays. For Illinois to be a good team, they need to constantly move the ball forward and create point-generating opportunities because teams like Michigan do that even when they blow out their opponent.
Chart 2: Offensive Player of the Game
For this chart, I am showing predicted points added per play. Every time a stat was recorded for the below players, the predicted points from the play were taken. The top performer here will be the offensive player of the game, which I will keep track of for end of season honors.
Chart 3: Defensive Player of the Game
While offensive stats are easy to create PPA for, defensive stats are harder. For this calculation, I took the average PPA for every opponent sack, interception, and pass incompletion. Forced fumbles will be added in later iterations.
Then I looked at the following stats for each defensive player; Touchdowns, QB hurry, Pass Deflection, Tackles for Loss, and Sacks. Fumbles and Tackles will be added in later iterations.
Then, I made the following calculations:
- Individual PPA TD = # of TDs (I don’t want to over represent TD’s)
- Individual PPA QB Hurry = # of QB Hurries * .5 * (average PPA Interception + average PPA Sack)
- Individual PPA TFL = # TFL * .25 * (average PPA sack)
- Individual PPA Sack = # Sacks * average PPA Sacks
The individual PPA is added up, and the cumulative PPA is shown below.
These numbers are negative since these players took points away (negatively added) from the opponent's scores.
Again, more qualitative analysis will be shared weekly to back up these numbers.
I’ll improve the data and visualizations, but I think this is a pretty good MVP — Minimum Viable Product.
Let me know what you think below.
Saturday can’t some soon enough.