G’s Exploration or Is the 2020 Formula Actually Better?

Greetings and happy Saturday. There are no football games today, but college football is still a hot topic. Today, I’d like to analyze the new formula for 2020. Late in the season, really during the beginning of the bowl games, I recognized an anomaly. I ran some what if scenarios and the results, quite frankly, make no sense. Specifically, while Clemson was a good team, it had them remaining in the top spot even if they lost the semifinal by 50 and Ohio State lost the championship by 50. At the same time, 4-loss Auburn, a good team with a seriously tough schedule (see the next blog for the top 25 in different categories lists), showed to be in the top 4. Something was clearly off. I wish I could say the GCR formula is easy to decipher. Let me be clear, I REALLY wish the formula was easier to decipher and debug. I finally found a formula that was created when I added the ability for teams to play twice (a complete rewrite) that caused the issue. The issue was, I’ll try to be succinct, that wins counted more than once. That’s not exactly it, but there was double counting going on. When I corrected the formula, things made more sense. The other change I made for next year is on a piece that doesn’t have a great impact, but could make a cumulative difference. Within the performance score is an adjustment, up or down, based on the score differential of the game. It’s premise is that the first point is the most important (in a 20-19 game, the team with 20 won – ergo most important). Each point after that is progressively less important. Once a team wins by 3 touchdowns, each additional point scored has almost no impact at all on the rating. But I realized as I was trying to decipher the formula, that the GCR didn’t recognize that beating an FCS team or a 2-10 team by 30 was not the same thing as beating an Oregon or Michigan or even a Louisville or Texas Tech by 30. The whole concept of point differential must have a diminishing returns factor in a 12-game schedule or a blow-out has undue weight (if I did this for major league baseball, the 162 games would wash out the occasional 12-2 win). That logic dictates that there needs to be an additional damper in running up the score on a bad team – effectively stopping the reward for beating up a weaker opponent. I made the formulaic change and the performance scores were impacted by around 1%, but I feel better because I believe them to be more accurate. By the way, the opposite applies to a weak team getting pummeled by the stronger. The point differential loss is lower in that situation than it was before.

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These changes impact both of the major outputs from the GCR: overall ranking and predictions. I talked about the overall rankings already – they make more empirical sense now. A top 6 of LSU, Ohio State, Penn State, Florida, Clemson, Georgia with Auburn at 12 is somewhat logical. It certainly makes more sense that the top 6 of Clemson, Georgia, LSU, Alabama, Florida, and Auburn. I think we can say, based on the 2019 season, the new formula passes the ranking test. For the prediction test, I looked at all 1552 games between Division I teams (I did not count FCS vs Division II or NAIA because I don’t predict those). I separated them by deciles (50.0-59.9, 60.0-69.9, 70.0-79.9, 80.0-89.9, 90.0+) and analyzed a couple of different ways. Let’s look at the 2019 formula first. By decile, I projected a win/loss. This is pretty straightforward. For the 50s decile, I assume an average win rate of 55%, 60s: 65% and so on. Therefore, the overall expected win rate is heavily influenced by the percentage of games in each decile. The more games in the lower deciles, the lower the overall expectation. Let’s look at last year using the 2019 formula.

Decile	Projected Record	Actual Record	Pct	Win Difference	Count	% of Total
50s	314-256	335-235	0.588	21	570	37%
60s	303-163	362-104	0.777	59	466	30%
70s	224-75	267-32	0.893	43	299	19%
80s	118-21	129-10	0.928	11	139	9%
90s	74-4	75-3	0.962	1	78	5%
Total	1033-519	1168-384	0.753	135	1552	100%

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Let’s start on the right hand side. Fully 37% of all games were predicted in the might-as-well-be-a-coin-toss 50s decile. 67% were less than 70% to win. I’ll be the first to say it: that’s really wimpy. I predict, with all of the volatility of college football, that 2/3 of the time, it’s almost too close to call. Seriously?!? That’s over a thousand games and they have an overall expected correctness rate of less than 60%. Geez, that’s just pathetic. More on that in a minute. Read across and there’s the expected record and the actual record (granted in hind-sight). In each decile, the winning percentage is above expectation. The difference in games won, at 135, is certainly acceptable, all things considered. Being right 75.3% of the time is…ok, I guess (remember, this is in hindsight so I would expect the number to be higher than the 66.8% live). That number is pretty interesting because it is similar to the projected percentage or 66.6%. Now, let’s take a look at the same games, with the same prediction formula, but using the 2020 formulaic changes mentioned above.

Decile	Projected Record	Actual Record	Pct	Win Difference	Count	% of Total
50s	239-186	271-164	0.623	32	435	28%
60s	220-119	269-70	0.794	49	339	22%
70s	213-71	253-31	0.891	40	284	18%
80s	181-32	199-14	0.934	18	213	14%
90s	267-14	275-6	0.979	8	281	18%
Total	1121-431	1267-285	0.816	135	1552	100%

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Again, starting at the right, we see a much more even distribution among the deciles. In this view, 50% of the games are close (less than 70% to win) compared to 67% before. And 32% are in major upset territory (80%+ to win) compared to just 14% before. Because of this we see an increase in overall expected wins from 1033 to 1121 (+88) which increased the bogie from 66.6% to 72.2%. Now the 75% from above doesn’t look as favorable. We see, as before, that each decile shows a better than expected result. And we see the overall actual win total jumping from 1168 to 1267 (+99), not only bringing the winning percentage to just under 82%, but a bigger increase than the expected result. Here’s a chart that compares the two sets of results.

Decile	2019 Record	Pct	2020 Record	Pct	Basis Points
50s	335-235	0.588	271-164	0.623	350
60s	362-104	0.777	269-70	0.794	170
70s	267-32	0.893	253-31	0.891	(20)
80s	129-10	0.928	199-14	0.934	60
90s	75-3	0.962	275-6	0.979	170
Total	1168-384	0.753	1267-285	0.816	630
Exp % Win	66.6%		81.6%		560

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Because the number of games is different in the deciles, before and after change, we must compare using basis points (a basis point is .01% so 100 basis points is a full percentage point). In this we see, with the exception of the 70s, each decile showed pretty significant improvement (not sure the 80s at just 60 basis points is significant). Overall we see a 630 basis jump compared to the expected 530 improvement just on better distribution of games.

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I think this shows that the new formula passes the prediction test. Next year, I’m going to test it live, of course, but also against Vegas (it would be much too difficult to complete that analysis at this point). Next year, we’ll put it to the test, but if this type of improvement carries through, it will be a fun season, indeed.

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That’s it for this week. I’ll see you next weekend for top 25’s. Please comment/question/challenge, and most importantly, share with others. Thanks, G