From a certain vantage point, one could make the argument this should be a pretty straightforward preview. Due to a combination of graduations, early exits, and transfers, Thad Matta has had to battle significant roster turnover in each of the past three offseasons. Over that span, the Buckeyes have won 10, 11, and 11 Big Ten games. This offseason we had more roster turnover, yet again. So, it would be pretty easy to predict 10 or 11 wins, and a comment about being on the right or wrong side of the bubble depending upon how many shocking non-conference losses Ohio State piled up.
But that ignores a couple of things. First, one cannot simply go by conference and raw record to judge team quality. In addition to an ugly non-conference slate that included a couple of humiliating losses at home, Ohio State sweated out multiple wins against the bottom of the Big Ten, and was essentially uncompetitive with the top teams in the league. The final kenpom rating—76—was far lower than anything ever achieved in the Thad Matta Era (previous low was 34).
And while last season was exceptionally bad, that's kind of the point—it was likely an exception. Matta annually welcomes too much talent to Columbus to put bad teams on the court as a matter of habit. And sure enough, Pomeroy's computer has this Ohio State team ranked highly—13—so surely, that's that and we can go back to seeing the Buckeyes as a protected seed on Selection Sunday.
But I'm not convinced. For one, Pomeroy's explanation on Ohio State is one that I don't think quite lines up with the facts:
Finally, there is no change on incoming freshmen. The top 30 or so have an impact on a team’s rating and beyond that the computer is mostly blind to newcomers. That’s not to say it can’t make some guesses, though. In fact, it’s kind of a fun challenge to predict the impact of recruiting classes without any information on the recruiting class itself. Things like basketball budget, conference affiliation, recent performance, and whether the coach is returning handle some of this. But history says you can also glean some information from what kinds of players have left a team.
_This is the case with Ohio State, who is ranked higher here than anywhere else. They had a young team last season, and the other indicators in the model are very positive. Furthermore, even though three rotation players transferred, those players were replacement-level quality for the Big Ten.
_The fact that they are leaving is viewed as a positive in the model because if those players thought they would get more playing time, they would stay. And if they don’t expect to get more minutes, then those minutes figure to be taken by better players, which often means better players are coming into the program even if those players are ranked highly by recruiting services.
_In Ohio State’s case, they have just one top 100 RSCI freshman joining the team, so the computer’s assumptions fail a bit with respect to the Buckeyes. Still, the news of transfers leaving Ohio State was not a bad thing and even without a stellar recruiting class, there’s a good chance the minutes that need replacing will end up being more productive this season than last.
Pomeroy all but admits the computer is probably screwing up. What I think it sees is something like what happened in the 2013-14 season to Kentucky. In the offseason prior to that year, not only did UK lose two starters to early entry and graduation, but two other starters transferred—and they were actually pretty good players, too (Ryan Harrow and Kyle Wiltjer). For a team coming off a disappointing NIT first round exit, this isn't an ideal situation. But, of course, those players transferred not because the program was on fire, but because Calipari welcomed a freshman haul that included Julius Randle, James Young, Aaron and Andrew Harrison, and Dakari Johnson. Harrow and Wiltjer read the news, of course (or, more likely, Calipari had a level-setting conversation with them), so they took their talents elsewhere. A similar thing happened the next season with Kansas, which saw talented underclassmen Andrew White and Conner Frankamp leave for nearby schools with more opportunity. I'm sure KU's class that included Kelly Oubre and Cliff Alexander factored into those decisions.
But when looking at the circumstances surrounding Ohio State, I'm not sure that's the case. While Matta certainly believes the program is better off without one or more of Daniel Giddens, Mickey Mitchell, A.J. Harris, and Austin Grandstaff, it's not like the incoming class is a monster one (on paper, at least), with just one consensus top-100 recruit. So, from my vantage, this looks more like Lickliter Turnover than Calipari Turnover.
But the good news is that while it's generally not good to hemorrhage top-100 sophomores without restocking the cupboard with high-level talent, the fact of the matter is that these guys did not play very much in Big Ten play last year. Grandstaff was gone by then, and the other three accounted for roughly 20 percent of the player-minutes. That's an entirely replaceable amount. Moreover, the best sophomore of the bunch figures to be JaQuan Lyle, and he's still on the team and figures to take a massive leap forward as he cuts down on turnovers and improves his outside shot.
Matta still has a 6-man rotation that's as good as any in the Big Ten, and he typically does not have any issues keeping a tight rotation. It's probably best to assume last season was an aberration, and that Ohio State will return to its usual success this year. However, while I respect Pomeroy for not reaching into the machine and executing a manual override, I'm going to peek behind the data. To me, this is more like a strong 11-win team, than a team that's going to finish atop the Big Ten standings.