So... that happened. And just like the two games before it, Iowa came out sluggish and fell to a ten point deficit right off the bat. They were only to sniff single digit differences later in the second half. It has felt like the Hawks have been slow to open, starting cold. And that’s been a theme throughout the season. I’m not sure where this issue comes from, but I have a feeling that we may see a starting lineup change to combat this. If I know anything at all (doubtful) about the game, I have to believe we may see Nicholas Baer starting against OSU, or at least coming in much more quickly. Less likely, but also possible is that we see McCaffery opt to let Peter Jok sit and rest some. There’s no chance Iowa competes for a regular season conference championship, but a healthy Jok for the Big Ten Tournament would be Iowa’s only chance to sneak into the NCAA tournament. But that’s a long shot.
However, for the sake of Jok’s health and effectiveness, I think he needs to see more sparing use for the next few games. We’ll see if Fran and Co. agree with me on Saturday.
I’ve been noticing that H.A.W.K.E.Y.E.S. is chronically underestimating score lines lately. Looking back at the code I used, I recognized that there was a bit of a disconnect in the structural equation model. Previously I was using field goal attempts, three point attempts, and free throw attempts to predict both the number of possessions and the score. However, a few days ago I thought to myself that the number of attempts is not nearly so important to the score as the efficiency of those shots. So starting with the Illinois game, I substituted FG%, 3P%, and FT% to predict the score after using attempts to predict the number of possessions. The result was that H.A.W.K.E.Y.E.S. pretty much nailed the score for the Illini, but missed on Iowa. That said, the error of those combined score estimates is much much lower than in previous predictions. So, from here on out, I’ll be using this tweaked model instead. It should lead to better, more accurate predictions of the score (the predictions for the number of possessions per game did not need any changes, as it has been surprisingly accurate). If anybody out there is interested, I can provide a bit more detail as to the formal presentation of the model itself. But because this is a sports blog and not a statistics course, I won’t bore you all putting the nitty-gritty details here. If there’s enough interest, I might go ahead and give you all a more precise walk-through of the model and the process I use.
So the Buckeyes come to town on Saturday and bring with them 5 players averaging double-digit scoring per game and 9 players with double-digit scoring per 40 minutes played. This team is the essence of offensive balance. Luckily, this team does not really seem to thrive on the three, which has been Iowa’s most glaring weakness, consistently leaving opponents free to take uncontested shots from long range. Instead, the Buckeyes like to make their opponents play inside and have been, unbelievably, worse than Iowa at defending perimeter shooters. This is where Iowa will need to focus to pull this game off. If the Hawks can make a few threes in transition to pull OSU’s bigs out of the paint, we should see Cordell Pemsl and Tyler Cook be able to move a little more freely under the rim.
So what does the newly revamped and updated computer think? After kicking the side of the computer and smacking the monitor a few times, the artificial intelligence in the basement does not like this match-up for the Hawkeyes, predicting a Buckeye victory over Iowa, 81-76. Gross.
- The computer model got a few minor tweaks, and seems to be a little better on the score line
- Iowa’s Current Record: 11-10 (3-5)
- Prediction against Ohio State: 76-81 (L)