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Basketball
Basketball
Monday, May 14, 2012
Phil looks at one component, to show how the relative rate of points scored by the home team is dependent on how “easy” it is to score per possession. The easier it is to score, the less the relative rate of points will be earned by the home team. The harder it is to score (the more things have to compound in order for a goal or point to score), then the larger the relative rate of points earned by the home team.
In basketball, the current rules have it that the home team ends up getting 52% of the points. But, Phil changes the rules so that more compounding actions have to happen in order to get points, and he can change that to 54% of the points goes to the home team. Or, he reduces the number of compounding actions so that the home team gets barely more than 50% of the points.
It’s still part of the overall theme of “confrontations”. For example, Nadal’s clay-court advantage is different if we just look at total points earned, as opposed to matches-won. If you count as a “win” each point earned, maybe Nadal wins 150 points on court, while losing 100 or something. That is, he gets 60% of all points scored. But, if you count as a win each game, then he might win say 70% of all games. If you count as a win each set, he might win say 80% of the sets. And if you count as a win each match, he might win say 90% of all matches.
So, you can change the rules, like Phil is describing, to control the home-site advantage. And the compounding effects of the confrontations is how you do it.
Friday, May 04, 2012
I think this is what this writer is trying to say.
Tuesday, April 10, 2012
I love these tracers.
Tuesday, April 03, 2012
This is just a crazy thought I had. I haven’t tested it. Maybe someone out there can run the numbers. You get 9 points for a win, and then one bonus point for every run differential up to a maximum of 6 bonus points. So, win by 1, and you get 10 points. Win by 6 (or more), and you get 15 points. So, if you win three games by 1 run, or you win two by 6, and lose the third game, you get 30 points either way.
There are three points to consider when constructing this:
1. How many points for a win, your base level (*)
2. What score differential to cap?
3. Do you give points for losing close games, or do you get zero points whether you lose by 1 or 6?
(*) In my case is 9, which is a pleasant number as far as baseball is concerned. I kind of did a trial and error to see what numbers would seem reasonable, and once I saw that I was coalescing toward 9, I decided that’s a good enough number. My guess is that you can do this for any sport, and set the number as the average number of points per game. In baseball, it’s about 9 runs per game (4.5 for each team). I’d bet you can do the same thing in NHL and use 6 as the base level, and then cap it off at half of that, so that the range in NHL would be 7 to 10 or 11. NFL is probably 42 points as the base, so the range would be say 43 to 63 (maximum differential of 21 points). NBA? I dunno, say have a base of 200 points, with a range of 201 to 220 or something.
So, I’d like to see a bit of work from the Straigh Arrow readers, if you like to play around with stuff like this.
Wednesday, March 28, 2012
Need to know more?
Monday, March 26, 2012
Great stuff from Phil, and I wish I would have thought of that a long time ago.
I’m not sure that Phil adjusted for year-to-year league changes in the mean. You should, because I’ve shown that there was some noticeable sudden changes in free throw%, after controlling for the identity of the players (that is, I kept the same pool of players in adjacent seasons, and compared the free throw shooting %). Either the ball and/or the rim may have changed.
Monday, March 19, 2012
Peyton Manning.
Friday, March 16, 2012
I’ll use a hockey example first, and then I’ll switch to baseball.
In hockey, goal scoring follows a Poisson distribution. If we have two distributions, one with a mean of 3.0, and another with a mean of 2.7, we can figure out how often the one with a mean of 3.0 will have a random value higher than the one with the mean of 2.7. Ties are broken down in sudden-death OT fashion. In this case, in a 60-minute game of a 3 goals per game team facing a 2.7 goals per game team, the better team will win 55.3% of the time.
Now, what if a game of hockey was only one period? Setting aside any “change of pace” argument, we can model this as simply a 1.0 goals per game (that is, a game is 20 minutes, or one-third as long as the standard game) team facing a 0.9 goals per game team. In that case, Poisson says that the better team will win 53.5% of the time.
As you can see, changing nothing about the sport other than the number of periods, we can drastically alter the home-site advantage. It’s all based on the number of confrontations. The longer the game, the more the confrontations, then, the more the gap in talent will override the effect of random variation.
If we look at how often teams are tied heading into the third period, which is the same thing as I’m talking about here with the one-period game, I’m sure we’d see this kind of result, that home-site advantage will drop proportionately as I’m showing here.
We can see that with baseball as well. Now, baseball doesn’t follow a Poisson distribution, but we can model it as well. A 9-inning game gives us a .540 win%, while a 1-inning game would give us a .520 win% (which is close to the empirical result).
We can go through this with any sport, and the same thing will happen.
This is most clear in tennis, where the chance of Federer, Nadal, or Djokovic losing a 5-set match to someone other than the other two guys is much smaller than losing a 3-set match. For example, say that the big 3 is up 2 sets to 1 against the #20 seeded player. What is the chance that they would end up winning one of their next two sets? It’s going to be pretty high. Now, suppose the #20 seeded player is up 2 sets to 1 against one of the big 3. What is the chance that this #20 seeded player is going to win one of his next two sets? I don’t know what it is, but it’s DEFINITELY less than when the roles are reversed.
If Tiger in his prime had a 33% chance of winning a four-round tournament, then what’s the odds of him winning a single-round tournament? It’s definitely less than 33%. Probably something like 10%. That is, if you looked at each day’s results, I’d bet that Tiger in his prime won something like 10% of his rounds (if he won 33% of his tournaments). Something like that.
So, when people compare the home-site advantage of various sports, and trying to explain why one sport has a “higher” advantage than another, it’s meaningless. It’s entirely dependent on the number of confrontations.
Thursday, March 15, 2012
I can just as easily say:
My problem is that the people evaluating statistics in sports are not experts in sports.
***
Given the choice, I’d rather listen to a ballplayer or a scout that knows nothing about statistics, than a statistician that knows nothing about sports. Ballplayers know instinctively, through experience, how to play. They know where to set themselves up on the field, based on the score, the base-out situation. They know how to pitch. They know this far far more than a statistician.
Statisticians are not experts in sports, and the media is not an expert in sports. Players and scouts ARE experts in sports. Those people know what they are talking about. You can incorporate their knowledge into your model, so that your model can reflect reality. And, you can point out any possibly shortcomings the players may have. But, these are small shifts, not seismic changes.
I couldn’t apply what I do to cricket or rugby until I’d be able to really understand those sports from a participant (player) view point. You have to be a subject matter expert first, before you can apply your technical skills.
Sunday, March 04, 2012
Looks like I was wrong. I was so sure he would only respond in academic circles. He writes of Hollinger:
And finally, Hollinger doesn’t respond much to criticism. Back in 2006 he responded to something I said once. But that was it.
Holinger simply doesn’t spend much time addressing the problems with his model. And I think that might be an effective strategy. Addressing your critics is something we encourage in academia. At least, at our academic meetings, discussants are assigned to each paper and these discussants are supposed to critique your work. This process is supposed to make the work better. But outsider of academia, I am not convinced addressing critics has the effect academics suspect.
Paul Krugman, for example, frequently addresses his critics. But he clearly is not well-loved by these same critics. The problem is that although Krugman addresses his critics, he clearly doesn’t agree with these people. And that is the problem for these critics. People don’t want their criticisms addressed. They want people to agree with these criticisms. When that doesn’t happen, well… these critics get very angry.
So I think Hollinger has probably taken the correct path. By essentially ignoring his critics he has defused a great deal of hostility. And that might have had a small impact on the popularity of PER.
So, it’s time for all those basketball analysts to write their criticisms of Wins Produced, since Berri will respond to them.
***
I love the work that Winston did here:
We found that
45.75*(Points/Minute)
+22.55*(Rebounds/Minute)
+32.8*(Assists/Minute)
+58.2*(Steals/Minute)
-48.65*(Turnovers/Minute)
-39.73*(Missed FG’s per minute)
-20.6*(Missed FT per minute)
+38.37*(Blocked Shots Per Miute)
-18.68*(Personal Fouls Per Minute)
explains over 99% of the variation in this season’s PER rankings and is off by an average of .37 in estimating the PER of the top 200 NBA players whose stats are on Yahoo.com. So basically our simple formula virtually duplicates the PER rating without a lot of mumbo jumbo.
I call it mathematical gyrations, but yes, running a regression on some very complex equation is a wonderful way to figure out what that thing is doing, if you get r=.99. Indeed, this is exactly what I (first) did with DIPS, when I ran a regression to get FIP. Voros went out of his way to do what he did (and it was mathematically correct, not mathematical gyrations in his case), but FIP got you almost all the way there.
You can do this with many things, when you see a bunch of components being added, multiplied, divided, raised to the power, and it’s not obvious what it’s doing, and why it’s doing it. Getting an r=.99 basically shines a bright light as to what it’s doing.
***
I only follow basketball analysis on the periphery, so feel free to provide comments on PER, Wins Produced, and whatever else you want to talk about.
Article and paper.
Glove-slap: Alan.
Thursday, March 01, 2012
When Middle-Management Attacks!
Monday, February 27, 2012
Interesting tidbits of information from Billy Hunter.
Monday, February 20, 2012
Not since John Malkovich entered his own brain have we seen such overuse of the word Lin.
Sunday, February 12, 2012
Dean Oliver throws his hat in the ring.
By reflecting a résumé, BPI was not explicitly built to make predictions. But, in tests, its ability to predict appears to be as good or better than Sagarin or RPI at predicting results in the NCAA tournament and NIT.
Between the 2007 and 2011 NCAA tournaments, it picked 74.4 percent of the matchups correctly, whereas Sagarin picked 73.2 percent and RPI picked 71.9 percent. (Kenpom is more difficult to evaluate because its pre-tournament rankings are not available.) The average ranking of the NIT finalists was better in BPI than in Sagarin or RPI. Notice, of course, that many of these differences are small. The BPI is not a guaranteed way to pick a perfect bracket, but we do think it is the best power ranking available.
Again, if Dean used in-sample data to test his system, it won’t be a fair test. Now, he did say he didn’t create the system against future data, but still, there’s going to be some doubt.
Andy also has a system, but I can’t tell if he provides historical data, or how he fares against the fellows above. I’ll ping him later and ask…
Saturday, February 11, 2012
The story begins:
This kid out of nowhere – out of Harvard University, out of the Reno Bighorns and Erie Bayhawks – had done it again, done it with a devastating 38 points, seven assists, four rebounds and two steals in the Knicks’ 92-85 victory over the Lakers.
“Players don’t come out of nowhere,” Bryant said.
What he was trying to say was this: The talent’s there, but sometimes the opportunity isn’t. It takes the right circumstances and timing, the right coach, right system. And sometimes, it takes desperation to try anything. And for these New York Knicks, well, Jeremy Lin constituted anything.
...
Twenty-four hours earlier, Bryant had been bemused over this Lin story. He wanted details, wanted to know the fuss. “Well, he’s got to deal with me now,” Bryant said.
...
No one in the history of the NBA has scored as much in his first three starts, but this has nothing to do with the statistics. It’s the feel, the touch, the spirit and the purity of it all. No Amar’e Stoudemire. No Carmelo Anthony. And it doesn’t matter what spare parts are thrown together with Lin because he’s elevated everyone, transformed five fingers into a fist.
“It’s a completely different team,” center Tyson Chandler said. “You can’t look at this team the same.”
...
Before D’Antoni had run out of players to try for these Knicks a week ago, before he had thrown Lin into a game with the New Jersey Nets, the Knicks’ front office had a decision to make: Do we guarantee Lin’s contract for the rest of the season, or release him with Tuesday’s deadline?
Knicks executive Mark Warkentien had been calling trusted associates in the NBA’s D-League, league sources told Yahoo! Sports, and asking them: Who does Lin play like? Who’s a good comparison? The Knicks had to make a decision based on old information, old scouting reports. And then, finally, D’Antoni dispatched Lin into the game against the Nets. Here was the answer, the unfolding of a week that has made Lin a sporting and cultural spectacle.
“A great story,” Bryant said. “It’s a testament to perseverance and hard work. A good example for kids everywhere.”
Those who worked with Lin in the D-League a year ago will tell you: He’s so grounded, so smart, so savvy, that he’s the perfect person to keep his bearings within a world exploding around him. Lin shrugs and simply says, “I am not really too worried about proving anything to anybody.”
And his wiki page reads as someone with steak and little sizzle, and recruiters prefer the sizzle.
Monday, January 16, 2012
Willie Mays, Jerry Rice, or Jim Brown. Poz Poll.
Impressed at the variety of readers at Poz’s site.
1.1 baseball players
0.9 football players
1.1 basketball players
0.9 hockey players
0.6 soccer players
Really, a pretty good spread right there.
Monday, January 02, 2012
A plan.
You don’t like it? Come up with your own.
Like how it is already? This thread is not for you.
Saturday, December 31, 2011
Here’s an example by a reader named Nick.
You play a 20 or 24-minute half. The score is 54-42.
To win the game, you need to score 96 points (54+42). So, when the second half starts, there is NO CLOCK. You simply keep playing, until some team scores 96 points. No more fouling at the end of the game, no more stalling the clock.
Two points: if the losing team at the half doesn’t score many points, you may end up having a quick game. So, one thing you can do is make sure that the final score needed is 50% higher than what the leading team has. So, in this case, any score from 54-0 to 54-27 would require the winning score to be 81 points.
Secondly: what kind of change in play will occur in the first half? For the team that’s leading, it’s in their interest to keep their opponent’s score low (rather than them scoring). After all, the number of points they need to score in the 2nd half equals the number of points their opponent has in the first half. So, I can see them go into a defensive shell. As for the trailing team in the 1st half, they need to score in the 2nd half the number of points the leading team has in the 1st half. So, I can ALSO see that team play in a defensive shell.
Basically, both teams are incentivized to play a defensive game. We’ll see first-half scores of say 30-27 or something like that.
I think. I’m not a basketball follower, and only played basketball during gym class.
What do you guys say?
I like the idea of not having a clock, but I’d like to see the best solution we can create. So, please offer suggestions as well.
Please make a positive contribution or otherwise constructive criticism.
Tuesday, December 20, 2011
Great stuff on the promotion/relegation system versus USA/Canada system.
If we look to Europe, though, we might see a better approach. To understand it, let’s consider the arguments of Frederich Hayek, who argued that a centrally planned economy can’t work as well as a free market one because the central planner could never have enough information to make adequate decisions. OK, but what does this have to do with sports?
Essentially, North American sports leagues use central planners to determine the location of sports teams. In contrast, European sports leagues rely on the market.
...
If these owners were ever successful, then essentially American owners would be exporting central planning to a market-oriented industry in Europe.
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