2014 Big Ten Football Season in Review - Game Script Analysis

Submitted by stbowie on August 18th, 2015 at 4:04 PM

Advanced Football Analytics puts together an interesting game summary, titled Game Scripts, for the NFL. Early last season I decided that I would put together these stats for the Big Ten, and here they are! (If this seems like an odd time to put together a summary of anything that happened in an actual football game last season, you’re not wrong. But it’s basically the last opportunity before there’s actual new football so… enjoy.)

What is a Game Script?
A Game Script (GS) is an average of the point differential for every second of a game. For example, if a team scores a field goal at the very end of the 1st quarter and that is the only score in the game (God save us from Big Ten football), the point differential was 0 for 900 seconds and +3 for the remaining 2700 seconds. The final point differential (Margin of Victory, or MoV) is +3 and the Game Script is +2.25.

Basically, gaining a lead early and maintaining it gives you a higher Game Script than keeping a game close and blowing it wide open at the end of the game. 

Why is it useful?
Useful is perhaps too high a bar to clear, as Game Scripts have little predictive value for future games, but it's certainly interesting and descriptive. A comparison of the final point differentials and Game Scripts can, at a glance, show which teams pulled off big comeback victories (a positive margin of victory, but a negative game script, indicating the other team held the lead for much of the game), which teams dominated the whole game (high game script and high margin of victory), and which teams played tight games but pulled away at the end (a low game script, but a high margin of victory). If you’ve watched a particular game from beginning to end, it doesn’t help much, but it does provide a good summary for games you weren’t able to watch in full.

I should be more clear when I say Game Scripts have little predictive value. Game Scripts correlate very strongly (~.94) with the final Margin of Victory for the same game - this should not be surprising, as both rely directly on points scored, i.e. you establish a lead, you maintain a lead, you tend to finish the game with a similar lead.


Average Game Scripts for the season correlate weakly with your results for the next game (~.28), and only slightly stronger than the correlation for average Margin of Victory for the season to your results for your next game (~.26). Sample size issues abound here, as we're looking at a single season and a stat that is only recorded once per game.

How’d Michigan do in this stat?
Rather dismally, I’m afraid, which is why instead of a mid-season project with weekly updates, this turned into an offseason summary project. As you are no doubt aware, last season Michigan lost regularly and emphatically. The good news is that Game Scripts offer plenty of schadenfreude to go around!

Game Scripts for all 2014 Big Ten Teams (In- and Out-of-Conference Games)

Team Table Chart
Illinois link link
Iowa link link
Northwestern link link
Wisconsin link link
Nebraska link link
Minnesota link link
Purdue link link
Indiana link link
Ohio State link link
Michigan State link link
Michigan link link
Rutgers link link
Maryland link link
Penn State link link

Which teams won in the most dominant fashion in the Big Ten this year (defined as biggest average Game Scripts in wins)?
MSU and OSU are the clear leaders in this category, with MSU averaging a 16.5 point lead in all of their wins (which includes a big late comeback against Baylor dragging their average down) and OSU averaging a 13.8 point lead in all of their wins. As we knew, they both had impressive seasons (certainly their last or close to last, right guys? Because Harbaugh?).

Some surprising teams are featured in the next echelon of this category, with Indiana, Minnesota, Michigan, Nebraska, Northwestern, Wisconsin, Purdue, Maryland, and Rutgers all averaging between one and two touchdowns in their victories. This is where strength of schedule (i.e. wins over cupcakes) and sample size (Indiana has a big game script in their wins… of which they had three) make a big difference. Michigan, for example, averaged a lead of 10.1 points in our wins, but that is anchored mainly by big wins over App State, Miami of Ohio, and Indiana. Similarly, Indiana won big over Indiana State and UNT, and squeaked by Mizzou with a 4-point victory. On the other hand, Wisconsin was just plain dominant in most of their victories this year, with the lone exceptions being their 2-point win over Iowa and their 3-point win over Auburn.

The last category has some of the teams you’d expect, with Rutgers, Illinois, Iowa, and Penn State all averaging a less-than-one-touchdown lead in their wins. Illinois deserves its own what’s-the-room-below-the-basement category, as its average lead in its five wins was a rather terrible 2.7 points.

This year’s biggest comeback wins (defined as largest negative Game Script in a victory)
MSU had the biggest comeback of the year against Baylor, with an average point differential of -7.8 for the game, and a 1-point final margin of victory. The next five closest were

  1. Rutgers over Maryland (GS: -6.7, MoV: 3)
  2. Minnesota over Nebraska (GS: -5.2, MoV: 4)
  3. The thoroughly embarrassing Iowa over Ball St (GS: -5, MoV: 4)
  4. PSU over Rutgers (GS: -4.6, MoV: 4)
  5. The absolutely glorious Northwestern over Notre Dame (G: -4.3, MoV: 3).

Michigan’s lack of any significant games in this category fits the narrative of the 2014 season – a lack of hope that this team could win a game once it fell behind. The lone comeback for Michigan on the season was against PSU, with a Game Script of -0.6 and a final margin of 5.

This year’s biggest comeback losses (defined as largest positive Game Script in a loss)
Wisconsin choking away its lead over LSU takes the cake in this category, with a Game Script of 5.9 and a final margin of -4. Not surprisingly, this category also features a few teams that were on the opposite end of the games mentioned in the section above – Nebraska, Rutgers, and Maryland were on the less glamorous end of those big comebacks. A miserable loss we wouldn’t want to overlook, however, is Iowa’s three point loss to ISU, with a Game Script of 4.1. Michigan’s only comeback loss was to Maryland, with a final margin of -7 and a Game Script of 1.2.

The depressing loss you knew you should have turned off in the 1st half but probably didn’t (defined as largest negative Game Script in a loss)
While Michigan’s embarrassing loss at Notre Dame was certainly its worst loss in this category, the good news is that that game doesn’t even make the top ten most depressing loss by a Big Ten team! Wisconsin leads off this category with its 59-0 loss to OSU in the BTCG, featuring a Game Script of -31.8. Other OSU wins (over Illinois and Rutgers) round out the top three. Wisconsin is the only team to both deliver and sustain a top-ten loss in this category (Maryland’s 45 point loss to Wiscy, with a game script of -25.3, comes in fourth on this list).

The dominant victory that made you feel all warm and fuzzy about the future of your team (defined as largest positive Game Script in a win)
Cupcakes fill out the majority of this category, with OSU’s win over Kent St (GS: 40.2, MoV: 66), MSU’s win over EMU (GS: 38.4, MoV: 59), and Wisconsin’s win over BGSU (GS: 29, MoV: 51) all placing in the top five. Michigan’s win over App State qualifies with a Game Script of 24.9, but falls outside the top ten.

Which games were closer than they seemed? (Game Script between -3 and 3, with a two-score win or loss)
Only seven games fell into this category:

  1. Illinois vs Purdue (GS: 2.5, MoV: 11)
  2. Northwestern vs Nebraska (GS: 2.1, MoV: 21)
  3. OSU @ Navy (GS: 2, MoV: 17)
  4. Illinois vs YSU (GS: 1.6, MoV: 11)
  5. Minnesota vs Mizzou (GS: -0.8, MoV: -16)
  6. Wisconsin vs Minnesota (GS: -2.7, MoV: 10)
  7. Purdue vs Iowa (GS: 0.6, MoV: 14)

I think OSU @ Navy and Minn vs Mizzou are the most interesting games from this category – the first because it shows OSU as a bit more fallible than they looked in the final score, and the second because the eventual SEC East Champion almost lost to two Big Ten teams that weren’t particularly close to the top of their divisions.

What does it all mean?
In perhaps the most obvious conclusion ever made in the history of sports analysis, scoring more points early (and throughout the game) is better and leads to more winning. Allowing your oppononent to score more points than you do in the later parts of games occasionally leads to unexpected losses. Quote me as frequently as you wish, and send all royalties to me by check via USPS.

Beyond that, I think you can argue that MSU had the more dominant season than OSU, given how close the GS was for their game, and the number of OSU games that featured low GS but high MoV (Navy, MSU, Indiana, Michigan, Alabama). Whether that's good news or bad news is up to you.

Happy to hear your comments and feedback on this analysis! In particular, I’d love to hear:

  1. How awesome you think this is;
  2. Suggestions on how to incorporate pass/run ratios into some of the above analysis;
  3. Suggestions on visual representations of the analysis;
  4. New categories to include in future versions;
  5. How to figure out those lightboxes for my image links...

I’m planning on continuing this in the 2015 season, and perhaps even providing weekly updates. If you’re interested, you can view the full Game Scripts data for the 2014 Big Ten season here. And of course, I urge you to check out the Advanced Football Analytics site (recently acquired by ESPN), which does all sorts of awesome analysis of NFL games.




August 18th, 2015 at 5:13 PM ^

Thanks, this is really interesting. As a follow-up, it'd be cool to do this for some of Michigan's big comeback wins (e.g. 2003 Minnesota, 2008 Wisconsin, 2011 Notre Dame) and see how they compare.


August 18th, 2015 at 6:57 PM ^

I thought about this, but I'm limited by what espn still has boxscores for (the excel macros I wrote rely on the specific formatting they use for recent seasons). The more recent games are a possibility - I looked into doing the 1997 season and that was a no go.


August 19th, 2015 at 1:34 PM ^

I thought of doing this many years ago for basketball--I didn't realize anybody actually implemented it.  So has advanced football analytics determined that there is no predictive value to it?  That's disappointing; I would have thought that it might--just on the concept that a 21-point win where the winner builds up a quick 35-point first half lead and coasts in the second half is more impressive than a 21-point win where the winner scores a last-minute touchdown after the game has been decided.

My idea for basketball was to tie it to an idea of "momentum":  Momentum = Points Scored - Game Script - Points Allowed.  So a team with a 10-point lead but a Game Script of +6.3 has a "Momentum" of 3.7.  I had the idea that as the momentum approaches zero, even if the game is not all that close at the time, we are looking at an important point in the game where things could end up going either direction.  But basketball is much more a game of momentum than football is.


August 20th, 2015 at 6:05 PM ^

I updated the post to include some correlations between average MoV/GS for the year and the MoV in the next game. GS is slightly more strongly correlated, but both are pretty weak. If we could include strength of schedule considerations in it, that might make for a stronger link and is something I'll consider for future posts.