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Published Dec 4, 2023
Dr. Green and White Hoops Analysis: Mathematical Season Preview
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Paul Fanson  •  Spartans Illustrated
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@PaulFanson

The 2023-2024 college basketball season kicked off just under a month ago and the action across the country is heating up. Action in the Big Ten Conference officially started off this past weekend, and a total of 12 conference games will be played in the month of December.

At the beginning of the season, the various prognosticators and pollsters had an idea of which teams would challenge for the Big Ten title and other postseason glory. But we now have a month of actual data about the relative strength of each team. It is time to put that data to work.

Throughout the Michigan State football season, I provided bi-weekly updates on the odds of various season outcomes. I utilized my own power rankings and a set of simulation and other analytical tools to generate these odds. My process for college basketball is very similar.

The major difference is that I prefer to use efficiency metrics, specifically those tabulated by Ken Pomeroy (kenpom.com) to estimate point spreads and odds. These data correlate well to point spreads and point spreads correlate to actual game results. There is no reason for me to recreate the wheel.

I have recently compiled data from the non-conference and performed my first simulation of the 2023-2024 Big Ten men's basketball season. The results of the simulation and other calculations can tell us a lot about how the Big Ten season will progress. Today, I will share what I found.

How Good is the Competition?

It may seem obvious, but the single most important factor in how the Big Ten season will shake out is the relative strength of each team. Good teams tend to win more games than not-so-good teams.

The best place to start in this analysis is to review the current Kenpom ranking of all 14 Big Ten teams, which I have summarized below in Figure 1.

Figure 1 includes both the current Kenpom efficiencies and rankings as of Nov. 30, as well as the rankings published by Kenpom on Oct. 15, prior to the season. Note that I conducted this analysis prior to the start of the Big Ten season, so it does not reflect Purdue's upset loss at Northwestern or the other games that occurred over the weekend.

Purdue is considered to be head-and-shoulders better than the rest of the Big Ten field, both in the preseason, and even more so as of Nov. 30. After the Boilermakers, Michigan State, despite a 4-3 record, grades out as the second-best team with a slightly lower efficiency than expected back in October.

There are currently three other teams in the same general tier as the Spartans. Ohio State, Wisconsin and Illinois are all ranked in the 20s in Kenpom efficiency margin. Iowa and Nebraska are both ranked around No. 40 nationally and make up the third Big Ten tier, rounding out the top-half of the conference.

The fourth Big Ten tier includes Michigan, Rutgers, Northwestern and Maryland, which are all ranked in the 50s nationally. After that, there is a steady decline in the quality of the last three teams: No. 72 Indiana, No. 96 Penn State,and No. 119 Minnesota.

Also note that there are a few teams that (so far) are notably better or worse than predicted in the preseason. Ohio State, Iowa and Nebraska are trending up. In contrast, Northwestern, Rutgers and Penn State are trending down.

Strength of Schedule

In an ideal world, the Big Ten regular season would be 26 games such that each team could face every other team twice, once on the road and once at home. Instead, there are only 20 conference games, meaning each Big Ten team will play seven opponents twice, three opponents at home only, and three opponents on the road only.

This creates an imbalance in the schedule which does benefit some teams, and which hurts others. But how big is this effect and which teams benefit or suffer?

Table 1 below some a matrix that summarizes the full Big Ten schedule.

The white cells in the matrix indicate teams that play each other twice. The green shaded cells represent the situation where there is only one regular season contest and the team in the row is at home. The yellow shaded cells represent the situation where the team in the row is on the road.

For example, Michigan State will play Ohio State, Rutgers, and Iowa at home only. The Spartans will face Purdue, Nebraska and Indiana on the road only. All other Big Ten teams will face Michigan State twice.

A glance at Table 1 gives an initial impression of the relative difficulty of each schedule. If a team's row (or column) has a lot of "2s" on the left-hand side (or top) and more "1s" on the right/bottom, this indicates a tougher schedule and vice versa.

Another slightly more quantitative indication is shown in the bottom row of the table. Here I tabulated the average efficiency margin of the opponents that each team plays only once. The higher this number, in general, the easier the schedule. This implies that the team plays more of the good teams just once and the weaker teams twice.

Fortunately, I have a more mathematically rigorous method to calculate strength of schedule. I use the concept of expected value and run a sort of experiment where a hypothetical top-25-quality reference team plays every Big Ten schedule. The question that I ask is "how many games would this reference team be expected to win?" Mathematically this is equal to the sum of the odds for the reference team to which each game.

Figure 2 below shows the results of this calculation as of Nov. 30.

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