If you haven't seen the highlight from the Iowa game you have probably been living under a rock. Yes, that one is on the list. The rest are some of my favorites. Feel free to add others if you deem them worthy.
Iverson, from UW vs Iowa
www.youtube.com/watch?v=whk7-F6qe8c
The others are from my memory, but you may be able to find video if you search youtube long enough.
Alando Tucker dunks off a pass from Devin Harris in the closing seconds vs Michigan State in 2002-2003 season. OK so the dunk itself wasn't anything special. Just a breakaway dunk with no defense around, but it's what Izzo said after the game that made this dunk huge. This was the "I'll remember that" game. More than annoying Izzo, this game announced that the Badgers were the real deal and started a string of victories against MSU and Izzo. Bo owned Izzo in these days.
Alando again, but this time vs North Carolina in the elite 8 game vs the Sean May lead Tarheels. This game was tight throughout and the narrative that UW didn't have athletes was even stronger in these days than today. After a timeout, Bo Ryan goes to a rare called play, alley oop dunk to Tucker. UW doesn't win the game, but UW shows it can run with the biggest and best.
I don't remember the year of this one, the team they were playing or if they won the game (I'm pretty sure they won the game), but if you are old enough you will know what I'm talking about. Before Iverson and Tucker, UW had another great dunker back when UW was just becoming a decent program. The ball goes up and it is a UW miss. The ball comes off perhaps a bit harder than Mike Finley expected. Nevertheless he grabs it with one hand and hammers it down, landing with feet spread to a shocked and rocking student section. At the time this was by leaps and bounds the best dunk by a badger ever. It still holds up as a great one over time.
Finally an alley oop that wasn't technically a dunk, but was still spectacular. It was January of 1990 and UW was playing hated rival Minnesota. There was 1 second left in a tied 75-75 ball game and the Badgers had the ball. Kurt Portman was the inbounder and threw the ball up high to give his team a chance. The best dunker of the Steve Yoder era, Patrick Tompkins, catches the ball and puts it in. I found this quote from after that game that summed up Patrick Tompkins game: "When I got the pass I was going to dunk it, but sometimes I have a tendency to dunk too hard and I didn't want to miss".
Friday, February 26, 2016
Sunday, February 14, 2016
Am I in love with Greg Gard?
My love of Bo Ryan is well known. It was the reason this blog was started. Over the past few weeks I have been questioning if I am becoming a polygamist with my basketball coaches. After Maryland, I'm falling for this guy. This lead me to think about Bo, and what lead me to fall in love the first time.
Bo's kids played entertaining basketball. They played hard on defense. They played efficient offense that forced the ball into the low post as much as possible (first through the swing and later through more iso). They scrapped for every loose ball. The players got better every year. These are all things I like about basketball, but most of all, they won. That's what makes a great coach. It's not so much how they win, but that they win.
If UW had hired a coach like Shaka Smart instead of Bo, and he had the same success with fast athletic recruits, I doubt I would have cared about any of the other reasons I liked Bo's teams. Winning is the biggest thing in sports (with the caveat that they have to be as clean as is needed to satisfy the NCAA, whatever clean means anymore). Bo figured out how to win better than any coach in Big Ten History, and he did it at a school that had never seen sustained success at that level.
So do I love Greg Gard? We'll see. His players are getting better and better as the season goes on. They play hard on defense and are routinely forcing the ball into the post with success. They have scrappers getting on the floor for loose balls. They have become fun to watch, but the proof is in the winning. Beating #2 on the road certainly helps his cause, but will he keep it up? My impression after 13 games is that he will.
He is certainly giving me hope that UW can sustain the success that Bo had here. Maybe not to the level that Bo did, but if he even gets close he will be one of the greatest coaches of all time. I remember when Barry retired and I thought that UW had probably hit the high mark they could reach. Then Bielema went to 3 consecutive Rose Bowls. Maybe Gard will take UW to new levels too. Here's hoping for the start of a love story for the ages.....
Wednesday, February 3, 2016
T-Ranketology
***Revamp in 2018!
As you'll see below, the original method of T-Ranketology was to take five ranks—T-Rank, Elo, WAB, RPI, and "Resume" rank—and given them various weights to come up with a score (with a couple other tweaks like a good record bonus and a bad record penalty).
Recently I wanted to come up with a way to use the T-Ranketology score to give an estimate of a team's chances for receiving an at-large. In looking into this, it occurred to me that I could use the five inputs to do a "logistic regression," and use the resulting model to provide the chance I was looking for, using the actual ranks themselves (rather than my own weighted score) as the input.
I did this, and the results were pleasing. So pleasing that I've now decided to abandon the old "weighted score" method and just use the model from the logistic regression all the way down. The results are pretty similar—after all, the inputs are the same—but I can do a bit more with the new system, and feel a bit more confident in the results. For example, on Teamcast I can now provide an estimate of how much a win or loss affects a team's chances, in percentage terms, of making the tournament.
So there you have it, that's the T-Ranketology update for this year.
***Another update:
See this post for some changes made in 2017.
***Update:
See this post for an attempt to retrofit T-Ranketology to fit last year's results. Next year's algorithm will be adjusted accordingly.
***Update on 2/17/16:
I've revamped the T-Ranketology formula a little bit, as I think the original version was more wishful thinking than predictive. Here are the changes:
1) I removed the current T-Rank as a factor. I put it in originally on the theory that this would be similar to the "Easy Bubble Solver" method which uses Kenpom ranking and RPI ranking to project likely tournament teams. But T-Rank is already a factor in that it is used to project the Elo and RPI ratings that make up the other two factors. Obviously, no one is looking at T-Rank (or Kenpom ranking, really) to determine who gets into the tournament.
2) In place of T-Rank, I've added a "good wins / bad losses" analysis. Teams get 10 points for top 50 wins, 3 points for other top 100 wins and lose 3 points for sub-100 losses, and 6 points for other sub-200 losses. (I may fiddle with these values some -- let me know if you have an idea what they should be instead.) Ideally I would use projected RPI as the source of the rankings, but I'm using current T-Rank because it's much easier for me. [Edit: after further review, it's not hard to use projected RPI, so I've fixed this.] I did this because "good wins" is clearly a big factor in how the people who actually decide this stuff go about deciding it.
The result is that I get a projected bracket that looks a lot more like the consensus. Valpo is no longer projected a 7-seed. Gonzaga no longer seems safely in. Princeton and Yale no longer look like serious bubble teams. Providence is no longer in serious danger of dropping out (although they're still much lower than in other brackets because of T-Rank's expectation that they'll lose some games coming home).
In my opinion, the former T-Ranketology produced a better bracket, but it wasn't as realistic.
*******
As promised, I've been playing around quite a bit with various T-Rank projects this year. Lately, I've added a couple of fun new rankings, including the Q-Rank (based on performance in Tournament Quality Tests), the H-Rank (ratings in last 10 games only) and the E-Rank, a slightly modified "Elo" rating system. I've also put together a program that uses T-Rank to forecast what teams' RPI will be at the end of the regular season.
My marketing people think I'm diluting the T-Rank brand with all these other rankings, but I have to give the people what they want, even if they don't know that it's what they want, yet.
Speaking of which, the latest: T-Ranketology. This is my NCAA bracket projections system. The inputs, in equal measure:
1) Current T-Rank
2) Projected RPI Rank at the end of the regular season
3) Projected Elo Rank at the end of the regular season
Both RPI and Elo are "resume" ranks, and T-Rank is a measure of team strength. The key to understand here is that the RPI and Elo inputs are projections -- that is, I run simulations of the rest of the season to get an average final RPI and Elo. (The T-Rank, however, is treated as a static determinant of team quality.)
So T-Ranketology is not a prediction based on "if the season ended today." It's a projection based on how the season will likely play out, if the current T-Rank is a correct measure of team strength.
One thing to make clear: my projection of the rest of the season does not include conference tournaments. I ain't got time for that.
Finally, I use current T-Rank to pick the autobids. That is, the highest team in the T-Rank is gifted the autobid, on the assumption (not always correct, given seeding effects) that the best team is most likely to win the conference tournament.
As always, this is all in good fun. If you see anything goofy, let me know.
As you'll see below, the original method of T-Ranketology was to take five ranks—T-Rank, Elo, WAB, RPI, and "Resume" rank—and given them various weights to come up with a score (with a couple other tweaks like a good record bonus and a bad record penalty).
Recently I wanted to come up with a way to use the T-Ranketology score to give an estimate of a team's chances for receiving an at-large. In looking into this, it occurred to me that I could use the five inputs to do a "logistic regression," and use the resulting model to provide the chance I was looking for, using the actual ranks themselves (rather than my own weighted score) as the input.
I did this, and the results were pleasing. So pleasing that I've now decided to abandon the old "weighted score" method and just use the model from the logistic regression all the way down. The results are pretty similar—after all, the inputs are the same—but I can do a bit more with the new system, and feel a bit more confident in the results. For example, on Teamcast I can now provide an estimate of how much a win or loss affects a team's chances, in percentage terms, of making the tournament.
So there you have it, that's the T-Ranketology update for this year.
***Another update:
See this post for some changes made in 2017.
***Update:
See this post for an attempt to retrofit T-Ranketology to fit last year's results. Next year's algorithm will be adjusted accordingly.
***Update on 2/17/16:
I've revamped the T-Ranketology formula a little bit, as I think the original version was more wishful thinking than predictive. Here are the changes:
1) I removed the current T-Rank as a factor. I put it in originally on the theory that this would be similar to the "Easy Bubble Solver" method which uses Kenpom ranking and RPI ranking to project likely tournament teams. But T-Rank is already a factor in that it is used to project the Elo and RPI ratings that make up the other two factors. Obviously, no one is looking at T-Rank (or Kenpom ranking, really) to determine who gets into the tournament.
2) In place of T-Rank, I've added a "good wins / bad losses" analysis. Teams get 10 points for top 50 wins, 3 points for other top 100 wins and lose 3 points for sub-100 losses, and 6 points for other sub-200 losses. (I may fiddle with these values some -- let me know if you have an idea what they should be instead.) Ideally I would use projected RPI as the source of the rankings, but I'm using current T-Rank because it's much easier for me. [Edit: after further review, it's not hard to use projected RPI, so I've fixed this.] I did this because "good wins" is clearly a big factor in how the people who actually decide this stuff go about deciding it.
The result is that I get a projected bracket that looks a lot more like the consensus. Valpo is no longer projected a 7-seed. Gonzaga no longer seems safely in. Princeton and Yale no longer look like serious bubble teams. Providence is no longer in serious danger of dropping out (although they're still much lower than in other brackets because of T-Rank's expectation that they'll lose some games coming home).
In my opinion, the former T-Ranketology produced a better bracket, but it wasn't as realistic.
*******
As promised, I've been playing around quite a bit with various T-Rank projects this year. Lately, I've added a couple of fun new rankings, including the Q-Rank (based on performance in Tournament Quality Tests), the H-Rank (ratings in last 10 games only) and the E-Rank, a slightly modified "Elo" rating system. I've also put together a program that uses T-Rank to forecast what teams' RPI will be at the end of the regular season.
My marketing people think I'm diluting the T-Rank brand with all these other rankings, but I have to give the people what they want, even if they don't know that it's what they want, yet.
Speaking of which, the latest: T-Ranketology. This is my NCAA bracket projections system. The inputs, in equal measure:
1) Current T-Rank
2) Projected RPI Rank at the end of the regular season
3) Projected Elo Rank at the end of the regular season
Both RPI and Elo are "resume" ranks, and T-Rank is a measure of team strength. The key to understand here is that the RPI and Elo inputs are projections -- that is, I run simulations of the rest of the season to get an average final RPI and Elo. (The T-Rank, however, is treated as a static determinant of team quality.)
So T-Ranketology is not a prediction based on "if the season ended today." It's a projection based on how the season will likely play out, if the current T-Rank is a correct measure of team strength.
One thing to make clear: my projection of the rest of the season does not include conference tournaments. I ain't got time for that.
Finally, I use current T-Rank to pick the autobids. That is, the highest team in the T-Rank is gifted the autobid, on the assumption (not always correct, given seeding effects) that the best team is most likely to win the conference tournament.
As always, this is all in good fun. If you see anything goofy, let me know.
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