The Big Ten conference schedule is unbalanced.

In a 14-team league, it would take 26 games to play a full round robin schedule home and away. Next year, the Big Ten will play 20 conference games (up from 18 this past season). That’s six games short of a round robin. What’s the impact of that unbalanced schedule and those missing six games?

Or to put it another way, who did the Big Ten scheduling office decide to screw the most with next year’s schedule?

Your favorite Big Ten stats guy is here to help answer that question.

Warning: Math ahead.

Step One: Who’s Good and Who’s Bad?

It’s a simple concept. If you want to win the Big Ten, it helps to play more bad teams and fewer good ones. So we need to identify who the good teams and the bad teams are.

I compiled a power poll of the league by averaging the 2018-19 Big Ten projections from T-Rank, BTN, and our very own Thomas Beindit. Results are shown below.

Michigan State Michigan Maryland Nebraska Wisconsin Indiana Purdue Ohio State Penn State Iowa Northwestern Illinois Minnesota Rutgers

Step Two: Calculating Expected Wins

To calculate expected wins, you need to come up with a Pythagorean score for each team. Lucky for us, we can just use T-Rank’s “Barthag” rating, which is exactly the same thing. T-Rank has data going back to 2008. Over that time, the average Barthag rating for the best team in the league has been .952.

(In layman’s terms, that means that the best team in the league will win 95.2% of its games against a random opponent on a neutral court.)

The average Barthag rating for the worst team in the league over the same period is .558. Because there weren’t 14 teams in the Big Ten all those years, I couldn’t calculate the average of each position. So to simplify things, I did a straight-line interpolation between first place and last place.

(In other words, the drop from first place to second place is .0304 points, the drop from second place to third place is .0304 points, etc., all the way down to fourteenth.)

Once we assign each team its Pythagorean score, it’s easy to calculate the probability that Team A wins a game against Team B. Just apply this formula, where a is Team’s A’s Pythagorean score and b is Team B’s Pythagorean score.

(a*(1-b)) / ((a*(1-b)+b*(1-a))

Add a small tweak to account for home court advantage, and it’s easy to simulate the results of every team against every other team. Under a full round-robin, Michigan State would be expected to win 21.9 games (out of 26) and Rutgers would be expected to win 7.0 games.

Step Three: Adjusting For Schedule

We then do the exact same thing, but remove the six no-plays for each team. See here for the full list of who plays whom and where. Under the actual 2019 schedule, Michigan State is expected to win 16.7 games (out of 20) and Rutgers is expected to win 5.7 games.

Interestingly enough, taking out the six games had zero impact on the projected order of finish. That’s probably an artifact of me spacing all the Pythagorean scores perfectly evenly. If you had two teams who were really close, the schedule may have had an impact.

Step Four: Comparing The Differences

You can look at the total number of “missed wins” from not playing a round robin, but the results aren’t all that interesting. Michigan State has the most (5.13) because they’re assumed to be the best team in the league. Every additional game has a high probability of being an additional win. Rutgers misses out only 1.34 wins by not playing those six extra games, because Rutgers is terrible. Or at least we assume they will be again in 2019.

You have to adjust both the “Round Robin” results and the “As Scheduled” results to winning percentages. Then you can see whose winning percentage dropped the most between the two sets of results.

The biggest drop in winning percentage was only .020, from .488 to .468. The biggest increase was only .014, from .271 to .285. The team with the biggest drop is the team who got screwed the most. Again, the drop isn’t much, and the projected order of finish under both sets of simulations was exactly the same as the power rankings above. So a 20-game schedule isn’t really all that imbalanced.

So Who Got Screwed

OK, enough math. You want to know who has the hardest schedule next season, and that teams is the Purdue Boilermakers. The Boilers miss Illinois, Iowa, and Rutgers—three projected-to-be-bad teams—on the road, and they miss Michigan, Northwestern, and Wisconsin at home.

Schedules ranked from Most Screwed to Least Screwed are as follows:

Purdue Michigan Maryland Penn State Minnesota Michigan State Iowa Nebraska Indiana Ohio State Illinois Wisconsin Northwestern Rutgers

Now before you say something like, “Michigan got an easy schedule because they don’t have to play Michigan!” let me remind you: Michigan didn’t have to play Michigan in either simulation. They have a hard schedule because of the six games they miss, five would have been against the bottom half of the conference.

I also ran the results flipping the home and away single-plays, and nobody moved more than a spot or two. Where you play somebody matters, but who you play matters more.

And even then, it doesn’t matter much. The unbalanced schedule projects to have zero effect on the overall conference standings. You can quibble with the power rankings that we started with, but you can substitute one team for another and the results are the same: the sixth best team (whoever it is) will be expected to win the sixth most games.

With that said... Yeah, Jim Delany totally screwed over my Purdue Boilermakers with this schedule. All the more reason to go from 20 conference games to 22.