Bracketology 2023

[Trotsky]

The problem with these predictive models is not that "conditions in the playoffs aren't like the range of season results," it's that they're extrapolating future probabilities from a system of ranking teams based on past performance.

That problem is because the future games aren't enough like the past games to justify extrapolation.

I'm not sure if you are being deliberately dense to have fun trolling (in which case, OK, I get that) or you honestly do not understand this.

[ugarte]

the issue is mostly that probabilities like this are unfalsifiable unless the projection is 100/0. i call this the Nate Silver Annoyance. the solution to this is to not care that much.

[Trotsky]

the issue is mostly that probabilities like this are unfalsifiable unless the projection is 100/0.

Whether and under what interpretation that would constitute falsifiability would be a really interesting philosophical question.

But it illustrates the problem perfectly.  If Cornell and Harvard were the only two teams, and met once in the RS to determine the home team for the NCAA title, and Cornell won in the RS, then our descriptive analytic for the RS would be 1.00 and Harvard would be .00.  It would not be theoretically justifiable to extrapolate this to a 100% prediction of Cornell winning the next meeting in the NCAA final.  That, in microcosm, is what is happening here when we take these numbers, or for that matter KRACH or PWR, as predictive of the tournament result.

But it's fun to spin up the trogs.

[Trotsky]

Also good call on the Silver Slugger.  If I hear one more idiot say Silver got 2016 "wrong"...  No, you morons.  He said a ten percent chance of dumbfuckery.  1 in 10.  And that's what we got: the 1 in 10.

[Beeeej]

Also good call on the Silver Slugger.  If I hear one more idiot say Silver got 2016 "wrong"...  No, you morons.  He said a ten percent chance of dumbfuckery.  1 in 10.  And that's what we got: the 1 in 10.

The same people who say he got 2016 wrong would not, if they were told by a statistician that there's a roughly 16.7% chance of rolling a six on a single die roll then shown a single die roll that lands on six, claim the statistician got that "wrong." But in politics, dumb gotta dumb.

[Dafatone]

Also good call on the Silver Slugger.  If I hear one more idiot say Silver got 2016 "wrong"...  No, you morons.  He said a ten percent chance of dumbfuckery.  1 in 10.  And that's what we got: the 1 in 10.

Wasn't his call closer to 3 in 10?

I will say that you have to differentiate between 538 the election prediction site and Nate Silver the twitter bad take generator.

[Trotsky]

Also good call on the Silver Slugger.  If I hear one more idiot say Silver got 2016 "wrong"...  No, you morons.  He said a ten percent chance of dumbfuckery.  1 in 10.  And that's what we got: the 1 in 10.

Wasn't his call closer to 3 in 10?

I will say that you have to differentiate between 538 the election prediction site and Nate Silver the twitter bad take generator.

19 sticks in my head actually.

Nate was wonderful until he wasn't.  But if you speak in public that many times, a few times are going to be awful.

That's just probability.

[ugarte]

Also good call on the Silver Slugger.  If I hear one more idiot say Silver got 2016 "wrong"...  No, you morons.  He said a ten percent chance of dumbfuckery.  1 in 10.  And that's what we got: the 1 in 10.

i definitely don't want to go on a tangent about silver but ... my point is that silverism is mostly bad not mostly good. the unfalsifiability of "1 in 10" and the general focus on the horse race over the substance (and silver constantly acting like the pundit he once claimed to be the antidote for is what drives me hold on i think i just went on a tangent please someone stop me from posting i don't want to do th

[abmarks]

For those questioning the Krach based predictions, particularly the chance of winning the ECAC tournament, here's a comparable analysis.

I was curious about the Tournament Champ distribution by seed but couldn't find the data easily; and even if I found it there'd be a small sample size.   What I did find was data from the 1985 to 2019 NCAA men's hoop tournaments.

With  64 team fields, split into 4 regions and teams seeded 1-16 in each, there are essentially four 16 teamers each year to reach the Final Four.  Look at 35 years of data and we can examine actual results from 140 of these 16-team tournaments.

Basketball and hockey are of course not the same, and I'm looking at 16 team fields rather than 12, but I think the distribution is informative nonetheless.

Teams that made the Final Four are effectively the champion of a 16 team field.  Looking at this it makes the 0% probability of winning for the bottom half of the ecac field look a lot more realistic since the bottom half in the hoop data shows zero to one percent for all but the 11-seed's three percent.





NCAA Men's Basketball tournament: 1985-2019
Final Four team seeds (or winners of 16 te.tournaments)

Code Select Expand


Seed MadeFF: Pct
        (Champ)
1 57 41%
2 29 21%
3 17 12%
4 13 9%
5 7 5%
6 3 2%
7 3 2%
8 5 4%
9 1 1%
10 1 1%
11 4 3%
12 0 0%
13 0 0%
14 0 0%
15 0 0%
16 0 0%
Total 140


[Trotsky]

Also good call on the Silver Slugger.  If I hear one more idiot say Silver got 2016 "wrong"...  No, you morons.  He said a ten percent chance of dumbfuckery.  1 in 10.  And that's what we got: the 1 in 10.

i definitely don't want to go on a tangent about silver but ... my point is that silverism is mostly bad not mostly good. the unfalsifiability of "1 in 10" and the general focus on the horse race over the substance (and silver constantly acting like the pundit he once claimed to be the antidote for is what drives me hold on i think i just went on a tangent please someone stop me from posting i don't want to do th

I don't know what Silverism is, but 538 was an excellent source of information on statistical analysis for lofo voters and bettors, er, sports fans, and it went the extra mile in demythologizing much of probability theory, in a way that still wasn't entirely wrong from a quant methods POV.  I give that an A plus.  I lay most of the blame for 538 being taken wrongly to other media who didn't know or care about nuance.  That wasn't the Two Nates' problem.

[CU2007]

For those questioning the Krach based predictions, particularly the chance of winning the ECAC tournament, here's a comparable analysis.

I was curious about the Tournament Champ distribution by seed but couldn't find the data easily; and even if I found it there'd be a small sample size.   What I did find was data from the 1985 to 2019 NCAA men's hoop tournaments.

With  64 team fields, split into 4 regions and teams seeded 1-16 in each, there are essentially four 16 teamers each year to reach the Final Four.  Look at 35 years of data and we can examine actual results from 140 of these 16-team tournaments.

Basketball and hockey are of course not the same, and I'm looking at 16 team fields rather than 12, but I think the distribution is informative nonetheless.

Teams that made the Final Four are effectively the champion of a 16 team field.  Looking at this it makes the 0% probability of winning for the bottom half of the ecac field look a lot more realistic since the bottom half in the hoop data shows zero to one percent for all but the 11-seed's three percent.





NCAA Men's Basketball tournament: 1985-2019
Final Four team seeds (or winners of 16 te.tournaments)

Code Select Expand


Seed MadeFF: Pct
        (Champ)
1 57 41%
2 29 21%
3 17 12%
4 13 9%
5 7 5%
6 3 2%
7 3 2%
8 5 4%
9 1 1%
10 1 1%
11 4 3%
12 0 0%
13 0 0%
14 0 0%
15 0 0%
16 0 0%
Total 140


Really good stuff, thanks. I think the 11 seed is a decent spot b/c in chalk you get a 5 then a 4 and by then maybe some chaos has eliminated some of 1-3 top teams for an easier path. Or it could just be a random coincidence. Of course the ECAC reseeds so there's a big difference Vs the basketball bracket approach.

[BearLover]

For those questioning the Krach based predictions, particularly the chance of winning the ECAC tournament, here's a comparable analysis.

I was curious about the Tournament Champ distribution by seed but couldn't find the data easily; and even if I found it there'd be a small sample size.   What I did find was data from the 1985 to 2019 NCAA men's hoop tournaments.

With  64 team fields, split into 4 regions and teams seeded 1-16 in each, there are essentially four 16 teamers each year to reach the Final Four.  Look at 35 years of data and we can examine actual results from 140 of these 16-team tournaments.

Basketball and hockey are of course not the same, and I'm looking at 16 team fields rather than 12, but I think the distribution is informative nonetheless.

Teams that made the Final Four are effectively the champion of a 16 team field.  Looking at this it makes the 0% probability of winning for the bottom half of the ecac field look a lot more realistic since the bottom half in the hoop data shows zero to one percent for all but the 11-seed's three percent.





NCAA Men's Basketball tournament: 1985-2019
Final Four team seeds (or winners of 16 te.tournaments)

Code Select Expand


Seed MadeFF: Pct
        (Champ)
1 57 41%
2 29 21%
3 17 12%
4 13 9%
5 7 5%
6 3 2%
7 3 2%
8 5 4%
9 1 1%
10 1 1%
11 4 3%
12 0 0%
13 0 0%
14 0 0%
15 0 0%
16 0 0%
Total 140


Really good stuff, thanks. I think the 11 seed is a decent spot b/c in chalk you get a 5 then a 4 and by then maybe some chaos has eliminated some of 1-3 top teams for an easier path. Or it could just be a random coincidence. Of course the ECAC reseeds so there's a big difference Vs the basketball bracket approach.

There is way less variance in a game of basketball than a game of hockey. I honestly don't think this is a very helpful comparison.

[abmarks]

For those questioning the Krach based predictions, particularly the chance of winning the ECAC tournament, here's a comparable analysis.

I was curious about the Tournament Champ distribution by seed but couldn't find the data easily; and even if I found it there'd be a small sample size.   What I did find was data from the 1985 to 2019 NCAA men's hoop tournaments.

With  64 team fields, split into 4 regions and teams seeded 1-16 in each, there are essentially four 16 teamers each year to reach the Final Four.  Look at 35 years of data and we can examine actual results from 140 of these 16-team tournaments.

Basketball and hockey are of course not the same, and I'm looking at 16 team fields rather than 12, but I think the distribution is informative nonetheless.

Teams that made the Final Four are effectively the champion of a 16 team field.  Looking at this it makes the 0% probability of winning for the bottom half of the ecac field look a lot more realistic since the bottom half in the hoop data shows zero to one percent for all but the 11-seed's three percent.





NCAA Men's Basketball tournament: 1985-2019
Final Four team seeds (or winners of 16 te.tournaments)

Code Select Expand


Seed MadeFF: Pct
        (Champ)
1 57 41%
2 29 21%
3 17 12%
4 13 9%
5 7 5%
6 3 2%
7 3 2%
8 5 4%
9 1 1%
10 1 1%
11 4 3%
12 0 0%
13 0 0%
14 0 0%
15 0 0%
16 0 0%
Total 140


Really good stuff, thanks. I think the 11 seed is a decent spot b/c in chalk you get a 5 then a 4 and by then maybe some chaos has eliminated some of 1-3 top teams for an easier path. Or it could just be a random coincidence. Of course the ECAC reseeds so there's a big difference Vs the basketball bracket approach.

There is way less variance in a game of basketball than a game of hockey. I honestly don't think this is a very helpful comparison.

It's not a single game comparison.   you have to win 4 games to win a 16-team tournament/bracket.

Also, in the ECAC tournament you've got to win a best of 3 in the second round. Best of 3 should significantly reduce variance and reduce upsets versus a single game round.

[marty]

There is way less variance in a game of basketball than a game of hockey. I honestly don't think this is a very helpful comparison.

Depends on whether you run up against a hot goal tender.::bolt::

[osorojo]

Two months ago on this site the practice of using records of past performance to predict winners of future games was reviled. Probability Puritans must be deeply disappointed by the total dominance of past performance used to select and seed teams in the finals.