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Been reading MGoBlog for 17…

Been reading MGoBlog for 17 years waiting for this post. Thanks for everything Brian.

Regarding the kPOY standings…

Regarding the kPOY standings, I think that Indiana's very good team defensive rating coupled with the minutes for Jackson-Davis are helping him out quite a bit. Kenpom mentioned in the 2012 kPOY intro post and after each time Russ Smith won the award that playing a lot of minutes for a very good defensive team is a large contributor to a high kPOY score as it's harder to distill the individual contributions the same way it is on offense.

Not exactly the right way to…

Not exactly the right way to look at those metrics - you're looking at the combined F+ rankings, which is an overall team rating. F+ doesn't split into offensive and defensive measures, but the component rankings (FEI and S&P+) do! I agree that this is a much better way to evaluate vs the per game metrics. Michigan's offensive rankings each of the last 4 years:

Year   FEI  S&P+

2015   27    34

2016   22    35

2017   70    49

2018   17    25

 

So, never a top 10 offense, and only once even close to a top 20 offense. Remember that these measures are all adjusted by opponent as well - the only non-P5 schools you see near the tops of these lists are ones that truly dominated the lower competition like UCF. I think S&P+ does a slightly better job of making these adjustments, but that's subjective. The top teams in these rankings are usually, the best teams/offenses.

Manziel and Mariota would

Manziel and Mariota would also qualify. I don't know if that was assumed, but it does bring the total up to 8 (the two mentioned in the previous sentence, two in the previous post, and Gardner) of the 29 accomplishing the feat in their first year. Out of curiosity, how many players on your list managed to accomplish the feat in multiple seasons or independent 5-game runs?

Thanks, fixed it. For all the

Thanks, fixed it. For all the times that I've read the guy's name, I should be able to spell it...

That's a very interesting observation. I'm throwing around the idea of going more in-depth on defense in the Big 10, looking more into game-by-game results. The impact of drawing two fouls on a starter would be a good place to start that analysis.

I would think that his

I would think that his hedging ability is a contributing factor to eFG% and, in a small way, tempo. Hedging denies an opponent an immediate open look and forces them to make additional passes to exploit the mismatch out of the hedge. The first half of the Western Michigan game had some great examples of opponents successfully making those passes against Michigan's hedging, but a well-executed hedge should deny the opponent their current set and force them to reset and execute their next option. This also takes more time off of the shot clock and increases the chances of a poor choice of shot or shot clock violation.

Based on observation, I'd say that the Michigan's hedging is more effective against less athletic teams that have to shoot over the mismatch, whereas more athletic teams can drive against or back down the mismatched player before Morgan can recover.

I actually looked at

I actually looked at regression when I started the analysis, but thought that the correlation numbers were easier to explain.

Regression shows that the four factors make for a very good predictor of Adjusted Defensive Efficiency (R^2 = 0.80) and an excellent predictor of Raw Defensive Efficiency (98%!). All four factors have p values well below 0.01, and adjusted R^2 is nearly the same as R^2 for both, so not a lot of noise when considering just these four variables. Not surprising that these statistics were given such a prominent position in the tempo-free community.

There is still a clear heirarchy of importance in the regression model, though. eFG% is still most important, followed by TO%, DReb%, and FT Rate in that order, with TO% and DReb% very close to each other and FT Rate lagging behind. However, with their importance beng approximately equal, the weight given to TO% in the regression model is greater than that given to DReb%, as the average value for TO% is 10 percentage points lower than that for DReb% (which is why I went with the correlation numbers).

Exactly. The total % for each

Exactly. The total % for each column adds up to the number of teams in that round x 100(%). That said, it looks like there are some rounding issues that make some columns add up to slightly more or less than their required amount, but nothing unusual.

Agreed on Turner. That

Agreed on Turner. That buzzer-beater was the worst sports punch-to-the-gut I've endured.

Bates-Diop would be a big

Bates-Diop would be a big get, but he's actually the class after Irvin, Walton, and Donnal. They're all 2013. Still, great way to kick off the 2014 class and take a player away from from Purdue/Illinois.

If Horford's knee is OK, we

If Horford's knee is OK, we might see:

Burke

Hardaway

Robinson

Horford

Morgan (or McGary)

 

Burke seemed to indicate at media days that Horford was playing the 4 in their "Big" lineup, so I'm interested to see how that shakes out.

Verdell Jones III graduated

Not that it really affects Indiana's status as the #1 contender