2018 ranking comparison

For the fourth year in a row, the TeamBrunnhilde linear model was the most accurate ranker in predicting the outcome of state tournament games.  The linear model for win-loss had an impressive debut, tying for second place with MaxPreps.  Fourth was a composite ranking.  WIAA RPI was fifth.  Everybody did a decent job of picking champions as their #1 ranked team.  Three rankers tied with eight #1’s that were champions: Evans Ranking, WIAA RPI, and TeamBrunnhilde linear model.  Lynden the top seed in Boys 2A won that championship, but was #2 in WIAA RPI.  Sorry, WIAA.  No mulligans.

I’ve read complaints that WIAA RPI doesn’t count Win-Loss enough.  Win-Loss as a ranking algorithm finished dead last.  I think there are better rankings than RPI, but RPI is an attempt to improve on Win-Loss with an understandable method for the less-mathematically inclined.  This year demonstrates that a properly done RPI does that.  Last year WIAA RPI had fatal flaws: not counting playoff games and using fake data for out of state teams.  With these corrected WIAA RPI finished 5th, well ahead of Won-Loss (which it trailed last year).

The TeamBrunnhilde linear model for win-loss, similar to TeamBrunnhilde linear model for points but with a different Y vector, did pretty good considering that the mathematics are not best suited for binary data (win-loss).  I’ll have to look at that before next year.

Everybody had successes.  Everybody had misses.  Not just at the top of the rankings.  We should all go back and work on slaying the variance monster next year.  But as Jesus might say, “The Random you will always have with you.”

Full results.

Rankings Comparison, etc.

Rankings Comparison

The 2018 State Tournament Ranking Comparison has seven rankers and a composite ranking this year. WIAA RPI is looking for improvement with changes made since last year’s last place finish. Of the first 96 games (regional round), 57 games are unanimous picks of the 7 rankers (the composite agrees to but then it has to by definition). Note that last year unanimous picks were wrong 20% of the time. So don’t be discouraged if you’re feeling dissed! And many those unanimous picks are just a ranking spot or two difference by various rankers.

If you propose a ranking, or use a ranking, you should be willing to have that ranking actually checked. You will likely find it isn’t ‘scary accurate’ after all. Every ranking will have notable successes and failures. The hubris of rankers will be tested. It is a reminder that games still need to be played. OK. Maybe not all games. Some of these are just flat out mismatches.

District Tournament notes

Hard work. I saw the first three girls winner-to-state games at Mount Tahoma on Saturday, February 10. Union played Bellarmine Prep in one of those. BP won by 32. But it was still a heckova game. Union fought throughout. After a tight (nearly) first half, they fell behind—but never ever stopped working. Sitting at Mount Tahoma that afternoon, knowing that the losers had more chances: who would be the ones that made it to state? Beamer. Rogers. Union? Yeah, Union. Hard work pays off. Congratulations to you! Next time I see you play, I’ll remember to bring my pickup truck to take home a load of bricks put up by your harassed opponent.

Boxing used to be a ‘State Championship’ sport. A banner in the Mount Vernon gymnasium proclaims the Bulldogs as 1952 State Champion in Boxing. I generally scan the banners in gymnasiums. What is the athletic tradition here? Sitting on the wooden built-in seats, ‘200-level’. Excellent sight lines to the floor. It is a beautiful old gym. You can totally imagine a boxing match here in 1952, floodlights on a ring at center court, like a 1950’s movie. The gym is the same. Glad I met its acquaintance, finally.

Sammamish has a new gym. A beautiful gym part of an athletic complex. It means the demise of the last ‘character’ gym in Kingco. Sammamish administrators won’t miss it, but I will. I only went there once a year, so didn’t notice the rats.

Another rating

I’ve lost track of the Associated Press high school basketball poll. The last one I saw was last season in late January. Haven’t seen one this season. Maybe it’s been abandoned. Don’t know how many of the voters saw more than a a few of the teams they were voting on anyway. Early weeks of the poll. which never started before January, always had their share of ‘booster votes’: votes for a local team that is sort of good and sure, why not them? You want to sell papers to the local folks, not somebody across the state. Nowadays the WIAA RPI is the ranking quoted when lauding a team. So the AP poll is obsolete. It was an example of ‘learned opinion’, maybe not very learned at all, but approximating a seeding committee, rather than a numeric algorithm.

There is something to winning games. That is ultimately what counts. I don’t like the way RPI uses that information, but when somebody is #1 in RPI one should take notice. In January at Wilson high school RPI #1 Gig Harbor (rated much lower by me) made Garfield (rated higher by me) look just average. I don’t think much of District 3 2A teams. Port Angeles is RPI #4. My linear model ranking has them #25. What would happen if the linear model just considered wins and losses: not margins. So modify the program to run the linear model with just 1 for the margin instead of the point spread. Port Angeles doesn’t rise to #4 but it makes the top 10.

Based through Sunday’s games (2/4/18) here is how the ranks compare, showing rankings for teams rated in the top 10 for at least one of three different rankings. LM-margin is the TeamBrunnhilde points rating. LM-win/loss is the LM-margin considering only winning and losing, not margin. WIAA RPI is, well, you should be able to figure that one out.

4A Girls

LM-margin

LM-win/loss

WIAA RPI

Central Valley

1

2

1

Eastlake

2

1

2

Moses Lake

3

4

4

Woodinville

4

3

5

Bellarmine Prep

5

9

6

Kentridge

6

11

3

University

7

15

20

Chiawana

8

6

10

Lewis and Clark

9

12

14

Sunnyside

10

8

12

Lake Stevens

11

13

7

Newport (Bellevue)

12

5

9

Skyline

13

7

17

Issaquah

18

10

25

Beamer

22

18

8

3A Girls

LM-margin

LM-win/loss

 WIAA RPI
Kamiakin

1

1

4

Prairie

2

9

7

Lincoln

3

5

2

Bethel

4

12

12

Garfield

5

8

8

West Seattle

6

2

5

Stanwood

7

13

15

Mount Spokane

8

11

25

Redmond

9

7

9

Snohomish

10

3

3

Gig Harbor

11

4

1

Bellevue

12

14

10

Seattle Prep

14

6

11

Edmonds-Woodway

15

10

13

Timberline

23

16

6

2A Girls

LM-margin

LM-win/loss

WIAA RPI

W.F. West

1

4

2

East Valley (Spokane)

2

1

1

Clarkston

3

2

10

Archbishop Murphy

4

6

3

Wapato

5

3

5

Burlington-Edison

6

7

6

East Valley (Yakima)

7

9

18

Lynden

8

11

8

Black Hills

9

8

7

Prosser

10

5

13

Mark Morris

20

15

9

Port Angeles

25

10

4

1A Girls

LM-margin

LM-win/loss

WIAA RPI

Cashmere

1

4

2

Lynden Christian

2

1

1

Zillah

3

2

4

La Salle

4

5

3

Medical Lake

5

3

5

La Center

6

9

7

Meridian

7

7

10

Cle Elum-Roslyn

8

10

18

Lakeside (Nine Mile Falls)

9

6

8

Nooksack Valley

10

12

13

Columbia (Burbank)

11

8

9

Elma

12

11

6

2B Girls

LM-margin

LM-win/loss

WIAA RPI

Colfax

1

2

10

Ilwaco

2

4

1

Davenport

3

1

2

Northwest Christian (Colbert)

4

5

16

St. George’s

5

3

7

Liberty (Spangle)

6

8

14

Napavine

7

10

3

Wahkiakum

8

11

4

White Swan

9

7

8

Mabton

10

6

9

Tri-Cities Prep

11

9

11

Life Christian

14

13

5

La Conner

15

14

6

1B Girls

LM-margin

LM-win/loss

WIAA RPI

Colton

1

1

1

Sunnyside Christian

2

3

4

Pomeroy

3

2

2

Almira Coulee Hartline

4

4

5

Oakesdale

5

6

12

Garfield-Palouse

6

10

16

Selkirk

7

5

7

Touchet

8

9

17

Inchelium

9

7

13

Wellpinit

10

8

18

Neah Bay

15

13

3

Mount Vernon Christian

17

14

8

Mount Rainier Lutheran

19

18

10

Puget Sound Adventist

23

19

6

Taholah

27

27

9

One gets different ranks depending on the algorithm used. No algorithm is inherently ‘right’. That is, God does not sit in His office with the ‘real’ rankings and we are searching for the algorithm the He uses. Come state tournament time, I’ll once again have a ranking competition. One would like to see validation for a ranking technique used for seeding that has a high rate of success in predicting the outcome of actual state tournament games.  If you want to participate with a set of rankings (192 teams, 12 tournaments, each with 16 teams ranked 1-16), leave a comment.  I’ll get back with you.  Russians eligible, too.

Tournament Month is Here!

February has been Tournament Month for me since 1996. My daughter and I attended several league games for Liberty (Issaquah) girls that season, and then stumbled into the D1 and D2 AAA tournament at Hec Ed quite by accident when we were up at UW for another reason. Haven’t missed Tournament Month since. From 1997 on it was the D2 3A (AA in 1997) girls tournament. That was where Liberty played through 2014, provided they qualified. I long felt that the D2 3A tournament was the toughest tournament in the state. But then I started delving into history and found some other tough nuts in the D7 and D9 B girls tournaments. Or how about the D5/8 4A girls tournaments? Any tournament with the Greater Spokane League girls is going to be a tough one.

One district that didn’t stand out as particularly difficult was District 3, West Central District. D3 4A tournament has always had a boat load of state berths. As whole leagues migrated into D3 (see Seamount moving from D2 to D3 in 2002), the number of berths ballooned in 2A and 3A classifications. But the number of actual GOOD TEAMs did not. For 2015 and 2016, D3 and D2 combined for 2A tournaments, Liberty participating. In 2015 Liberty was a good team, and after getting past White River, the only reliably good 2A girls team in D3, won the district title. Sammamish, the other D2 team was runner-up. I saw, up close, a lot of mediocre teams in that tournament. And a bunch of them made state. In 2016, Liberty was one of those mediocre teams, and they waltzed through into a third-place state berth. At state regionals, every one of those six teams (White River included) face-planted against the rest of the state.

So I’m not bullish at all on D3 girls basketball, even if they get a champion through now and then. There are good teams in D3, just way too few for the number of state berths handed to them by the WIAA geographical quota system.

In lieu of a weekly top 10 article, and in honor of Tournament Month, I’ve made up a chart projecting which girls teams make it to state, and who doesn’t, based on WIAA allocations and projecting success based on the TeamBrunnhilde points ratings. Of course they’ve got to win the tournament games. Bracketing affects that, too. What stands out is the number of teams outside the TeamBrunnhilde top 16 that will make it to state from District 3 (or combined district tournament involving District 3). There is also the enshrining of Emerald City mediocrity in the D1/2 1A tournament, where the Emerald City champion is automatically qualified to state and dropped into the district championship game. This year’s Emerald City best team, Seattle Academy, is currently rated #37 in 1A by me. That is not only dumb, it is grossly unfair: to make state in a mulit-league district tournament without having to even play anyone outside your league. Meanwhile eight 1A teams from District 5 rated above Seattle Academy will be sent home at or even before districts. Looking at the chart as a whole, there are 96 teams qualifying to state. There are 100 other teams that are rated above a team qualifying to state from another district, that will NOT be making it to state. And that is a best case scenario.

Just Play Fair. The WIAA motto. Is it fair that bad teams make it to state depending up where the district boundaries are drawn? I am absolutely opposed to basing state qualifications on ranking algorithms. But there needs to be some adjustment to the WIAA allocations that recognize merit. Districts that regularly produce good teams need to have more state berths allocated to them. It is not a transient thing. I’ve got 30 years of girls data and D3 has been pretty bad for most of those years. And the districts around Spokane have been pretty good.

Cross-classification scheduling

A recent post prompted an out-of-band exchange regarding scheduling games between classifications. The effect that scheduling itself has on rankings is a subject of interest to me. It is complex and requires a lot more time that I can devote during the season when, on some days, I spend five or more hours getting the previous day’s games into my database. But since I have that database I can ask it questions. It takes some time to formulate just how to ask, and understand just what that answer is telling me.

One question prompted by that exchange was just who schedules non-league games with teams in other classifications. League games are massively scheduled between teams in small subsets of the approximately 380 schools fielding varsity basketball teams. There really isn’t any point in using a ranking algorithm to determine who is the best team in a league when everybody plays everybody else one, two, even three times. That’s what the win-loss record is for. It is very easy to understand. The schedule is balanced, in most cases. The schedule for non-league games is not at all designed to even have the appearance of randomness. Thank goodness. I don’t want to see a random set of games selecting a 3A team to play a 1B team. I know that the only way Chief Kitsap girls will beat Bethel would be for the Tacoma Narrows bridge to collapse again while Bethel is driving across. The stat geek in me would find an experimentally designed schedule appealing. The fan in me would not. We get the non-league games that happen. They form the critical schedule structure for driving rankings of teams in different leagues and districts.

For starters, I looked at girls data for 2006-2007. That is the first season with the six current WIAA classifications. I did a lot of this by hand so hope I didn’t mis-transcribe. The inter-classification W-L table looks like

v 4A

v 3A

v 2A

v 1A

v 2B

v 1B

4A

112-51

14-14

0-1

0-1

3A

51-112

34-45

3-3

3-1

1-0

2A

14-14

45-34

68-34

9-5

3-0

1A

1-0

3-3

34-68

55-50

14-9

2B

1-0

1-3

5-9

50-55

81-84

1B

0-1

0-3

9-14

84-81

Looking at the yellow cells, where the classification difference is at least two, there are 81 games. The higher classification team won 47 and the lower classification team won 34. This season is also interesting for how badly 3A fared against adjacent classifications, but I didn’t look at those. Examining the 81 games was enough for me this week. Leave that for another day. Here is the list of the 81 games.

Thirty 4A teams scheduled down two or more classifications (maybe some teams count more than once) In so doing 4A went 14-16. Using 2006-2007 TeamBrunnhilde points rating, eight of these were in the top half of the classification, 22 in the bottom half, 16 in the bottom quarter. Clearly not a representative slice of 4A teams doing battle with 2A, 1A, and 2B teams. Of the 28 2A teams that scheduled up by two classifications (all against 4A), half were from the top half of that classification, 7 in the top 10. 2A fielded a lot better lineup going up against 4A.

Look at 3A. Eleven teams scheduled down. Three in the top half (#24 twice and #27), eight in the bottom half and six of those in bottom quarter.

2A scheduled down 14 times. Only three of the 14 were from the top half. Four were from the bottom three 2A teams. Klahowya, #54 (last), an epically dreadful team in the midst of a four-year period in which it won one (1) game, managed to lose twice to #60 1B team, Quilcene. Quilcene, though, rated 3 points ahead of Klahowya, so these were not upsets.

Fourteen of 1A teams scheduling down came from the top half of the classification. More than half. But where do those 1A teams live? Four games for Colfax, others from eastern Washington. People may be sparse; good girls basketball teams aren’t. Next town over might be 1B but the team is good. Seattle Christian (18) and Forks (21) were the only top-halfers west of the mountains. Okanogan (17) lost to Entiat (1B #6) twice: Entiat a six-point favorite anyway.

Overall for the 81 games, 28 down schedulers came from the top half of their classifications (4A, 3A, 2A, 1A); 45 up schedulers came from the top half of their classifications (2A, 1A, 2B, 1B). This is what you would expect. Coaches don’t want to schedule a slate of mis-matches.

If you had two hats, one with names of teams, from say 4A and the other from 2A, and pulled a name from each hat: who would win? Knowing nothing else, I would guess the 4A team. But if you had a hat with the games that were actually scheduled and asked who would win, that’s a different question: 50-50 for this season. It isn’t a random schedule–not for any of the cross-classification comparisons (2 or more differences). In theory, giving extra credit to the lower classification team winning or even playing the game seems logical. In practice it doesn’t necessarily work out.

But that’s not the end. What about the adjacent classification games? Funny that I picked 2006-2007 because that looks really interesting. What about next year, and the next? Just because something shows up one season doesn’t mean it applies to others. Now that there is the RPI incentive to making schedule arrangements, how has that changed scheduling? Then there is geographical asymmetry. Good teams are not evenly spread across the state, but most games are close by, posing difficulties for making cross-state comparisons. Lots of questions. But I’ve got a game to catch this evening.

Cross classification wins and losses

A recent commentary by Tim Martinez in the Vancouver Columbian whined about the Prairie girls being ripped off by the RPI rating the third week into the season. I agree that Prairie is a lot better than the RPI rank. However the column wandered from there into ‘fixing’ RPI by including factors for classification. So is this a problem? Are crappy teams in a higher division gaming the system (intentionally or not) by beating up on even crappier teams in lower divisions? When a 4A team plays a 1A team should the 4A team be penalized?

Looking at last season here are the cross-classification W-L record for girls.

v 4A v 3A v 2A v 1A v 2B v 1B
4A 115-100 26-24 1-6 1-0 1-0
3A 100-115 81-60 11-21 2-3 0-0
2A 26-24 60-81 73-77 8-10 3-2
1A 6-1 21-11 77-73 56-38 25-17
2B 0-1 3-2 10-8 38-56 73-55
1B 0-1 0-0 2-3 17-25 55-73

Let’s look at those 1A v 4A games. Only seven, but non-league games are not scheduled according to randomized experimental design anyway. La Center had two wins over bad Heritage and Battle Ground teams. I guess those crappy 4A teams in Clark county should look for an real 1A pushover instead of La Center. Or Zillah beat Davis. Seattle Christian beat Auburn and Mount Rainier. Good 1A teams beating not good 4A teams. Cascade Christian beat Federal Way: a mediocre 1A team over a bad 4A team. And then Sunnyside beat Zillah. A good non-league matchup between two good teams, one is 4A and the other 1A, but both good.

How about this year? Here’s the table for games so far:

v 4A v 3A v 2A v 1A v 2B v 1B
4A 69-62 27-22 4-4 0-0 0-0
3A 62-69 65-47 14-13 1-1 0-0
2A 22-27 47-65 42-45 9-3 1-2
1A 4-4 13-14 45-42 62-25 16-12
2B 0-0 1-1 3-9 25-62 33-42
1B 0-0 0-0 2-1 12-16 42-33

Best game I’ve seen so far was Sunnyside Christian (1B) at Lynden (2A). Sunnyside Christian won, not an upset. Not a black mark against Lynden for losing. Tim Martinez would put the ‘scarlet RPI’ on Lynden’s warmups for even scheduling that game. Maybe Hudson’s Bay has played several lower classification teams. But they look to be teams roughly well matched to Hudson’s Bay. You want to schedule some games that are likely wins where you can successfully exercise your skills against a live opponent. You want some games that will be challenges. And other games where you’re well matched. Regardless of classification.

Should a ranking method specifically include classification as a factor? There is a wide spread between good and bad teams within a classification, and a great deal of overlap between capabilities of teams in differing classifications. RPI is already a Rube Goldberg ranking. Hammering in a classification factor that doesn’t reflect reality would just make it worse.

Rating Debut

Mondays are a light day at TeamBrunnhilde. No slate of Sunday games to enter into the database. Time to catch up on missing games from the previous weeks.

A new feature of the schedule pages are TeamBrunnhilde point rating differences of the teams playing. Early in the season, before this season’s points ratings are calculated and published, that difference is based on last season. Consider Dayton girls, 4th in state last year and ranked #1 for part of the season by AP. They scored two (2) points in their opener against River View. That’s falling off a cliff. Mercer Island girls, last year’s 3A champion, who regularly beat hapless teams last season finds itself on the other side of the ‘hap’ ratio, losing their last three by 41 point average. Bothell girls ranked #1 by WIAA RPI are also in dumps, although that slide may have begun before last season ended. And on the boys side, one-and-done Nathan Hale finds itself descending to customary status for that program now that Brandon Roy has set up shop at Garfield. So last year’s ratings may be misleading.

So when to publish ratings based on this season? The TeamBrunnhilde points rating needs teams to be ‘connected’ into a continuous set. Say we measure the distance between teams by games. The distance of teamA to teamA is 0. You probably would have guessed that. From teamA to teamB, which have played each other, are 1 game apart. TeamB’s opponents are 2 games away from teamA (provided that teamA has also not played them). Imagine then a tinker-toy structure. Teams are the hubs and games are the colored rods connecting them. Early in the season, there are few games per hub. At first, there are pairs of hubs connected by single rod. As more games are played more and more connections are made, and finally every hub is connected, via other hubs and games, to every other hub. But the whole structure is pretty rickety. Measuring the width of the whole set, it might be 15 games or so between some teams. And the number of path ways for that distance will be few. By the end of the season the distance between teams will be well under 10 with multiple paths of comparison.

Last Tuesday (December 5), complete connection of teams was achieved; teams that hadn’t played sufficient games to connect to anybody not included. Wednesday, Thursday, Friday, and Saturday added more games. So by now, after two weeks of data, one can pick up the tinker-toy construction of games and it doesn’t sag too badly. It will get better. It is likely better than applying last year’s ratings to this year’s teams.

Here are the initial rankings, broken out by gender and class. This week’s complete rankings are available for boys and girls.

Girls 4A

1 Kentridge 55.5
2 Moses Lake 52.5
3 Eastlake 49.2
4 Lake Stevens 46.2
5 Bellarmine Prep 46.0
6 Woodinville 43.3
7 University 43.1
8 Chiawana 41.4
9 Lewis and Clark 37.7
10 Skyline 35.5

Girls 3A

1 Kamiakin 53.0
2 Garfield 51.5
3 Lincoln 46.6
4 Bethel 46.0
5 West Seattle 45.1
6 Seattle Prep 39.0
7 Rainier Beach 37.4
8 Bellevue 35.5
9 Prairie 34.4
10 Snohomish 34.3

Girls 2A

1 W.F. West 49.0
2 East Valley (Spokane) 41.8
3 Archbishop Murphy 40.5
4 Wapato 40.2
5 Clarkston 31.0
6 Black Hills 30.0
7 Burlington-Edison 29.5
8 White River 28.2
9 Woodland 25.0
10 Washougal 24.7

Girls 1A

1 Lynden Christian 52.5
2 Zillah 52.4
3 Cashmere 44.8
4 La Salle 35.6
5 Medical Lake 35.0
6 Cle Elum-Roslyn 34.8
7 La Center 31.1
8 Meridian 26.4
9 Connell 24.8
10 Seattle Christian 23.9

Girls 2B

1 Davenport 37.3
2 Northwest Christian (Colbert) 29.7
3 Mabton 28.9
4 St. George’s 25.7
5 Ilwaco 25.3
6 Wahkiakum 20.1
7 Lind-Ritzville/Sprague 19.7
8 Colfax 18.6
9 Napavine 16.9
10 White Swan 14.4

Girls 1B

1 Sunnyside Christian 56.1
2 Colton 23.7
3 Pomeroy 17.8
4 Inchelium 14.6
5 Garfield-Palouse 14.4
6 Oakesdale 9.2
7 Touchet 8.9
8 Selkirk 7.4
9 Wellpinit 5.5
10 Neah Bay 2.0

Boys 4A

1 Federal Way 57.8
2 Gonzaga Prep 45.1
3 Enumclaw 44.9
4 Richland 41.0
5 Bothell 39.2
6 Kentwood 38.2
7 Auburn 37.2
8 Curtis 36.4
9 Kamiak 36.1
10 Union 34.6

Boys 3A

1 Seattle Prep 61.2
2 Garfield 60.2
3 Eastside Catholic 52.7
4 Wilson 52.6
5 Rainier Beach 51.9
6 O’Dea 49.5
7 Lincoln 48.1
8 Franklin 46.9
9 Cleveland 42.0
10 Lakeside (Seattle) 38.5

Boys 2A

1 Mountlake Terrace 50.1
2 Anacortes 36.4
3 Columbia River 35.6
4 Foss 33.6
5 Selah 31.8
6 Sedro-Woolley 31.4
7 Lynden 31.1
8 Mark Morris 28.4
9 Lakewood 27.3
10 Renton 27.0

Boys 1A

1 Lynden Christian 35.1
2 Bellevue Christian 21.0
3 Montesano 19.6
4 King’s 17.4
5 Northwest School 13.4
6 Nooksack Valley 11.2
7 Freeman 10.1
8 Okanogan 9.6
9 South Whidbey 8.9
10 Mount Baker 8.4

Boys 2B

1 Winlock 23.0
2 Toutle Lake 22.1
3 Life Christian 21.5
4 Adna 14.6
5 Napavine 13.9
6 St. George’s 13.7
7 Brewster 13.1
8 Morton-White Pass 7.0
9 Toledo 3.7
10 Wahkiakum -3.3

Boys 1B

1 Sunnyside Christian 12.9
2 Muckleshoot 9.6
3 Yakama Tribal 8.6
4 Odessa 3.7
5 Cedar Park Christian (Mountlake Terrace) 0.7
6 Pomeroy -1.9
7 Naselle -5.5
8 Wellpinit -6.2
9 Tacoma Baptist -8.3
10 Mount Rainier Lutheran -8.4

On oddity is that the overall top girls team is Sunnyside Christian, a 1B school. This is likely an early-season artifact due to the paucity of games played. Sunnyside Christian had played only White Swan and Mabton. The latter two have only played, besides Sunnyside Christian, 1A teams from the SCAC. The SCAC has #6, #26, #31 overall (out of 357 teams with point ratings). This is likely due to a very small sample size of game connecting that set of teams with the rest of the set. So if one is inferring that Sunnyside Christian would beat Kentridge, the initial #2 team, don’t bet the mortgage money on that. However, within the SCAC the estimates are likely more reliable. Note also that Central Valley girls have played only one game so is not included in the TB point rating. Several other teams also fall into that category.

Upsets

I’ll admit to being a Liberty HS (Issaquah) girls basketball fan. Long ago, although I can remember, when Liberty made the state tournament in 1999, they played their winner-to-state versus Hazen, the next high school over.

Now Hazen finished second in Seamount that season, which doesn’t sound so bad. In the district tournament they played Holy Names, considered one of the hot teams in the state that season. Holy Names was Metro #1 and Hazen was Semount #2. Hazen won. “What a huge upset!” was my thought. But other than my opinion that Seamount girls basketball was mostly crappy and Metro mostly not (at least in the top half of the league) I had meager data to back it up. I needed to formulate criterion to determine what constituted an upset.

It took nearly fifteen seasons and acquisition of lots of data that I didn’t have in 1999, but I came up the the TeamBrunnhilde points rating, calculated historically now from 1988-1989 on. Using the TB Points rating calculated after the season, one can determine what games were won by teams with lower ratings. These can be classified as ‘upsets’.

Not every such ‘upset’ is much of a surprise, however. If Richard Nixon high school has a 23.80 points rating, and Lance Armstrong high school is rated 23.77, is an Armstrong win over Nixon an upset? Technically, yes, but practically, no. One would say Nixon and Armstrong were pretty well matched. But the points rating, shown to have a track record better than most for picking winners in state games correctly, is a reasonable start to assess upset magnitude.

Nowadays, when I attend a game, I consult my TB Points rating to estimate the final margin (point spread for when betting on girls’ high school games is legalized). What does that mean if Richard Nixon HS is rated +7.83 over Harvey Weinstein HS? How likely is Nixon to kick Weinstein’s ass? Or just win?

So I looked at my database for girls’ games 1988-2017 (as of October 10, 2017). In all, I’ve recorded 103,779 girls’ game results where I have ratings for both teams. The lower rated team won 14.07% of those games. Upsets if you will. However, most of those upsets occurred at fairly low ratings differences. For very closely rated games (ratings differences less that 1 point), the lower rated team won 46.97% of the time. Out of 4245 games, that beats flipping a coin. Not until one gets to about a 12 point difference does the upset percentage drop to 10%. That is for games with TB Points ratings differences of between 11 and 12 points, the lower rated team wins 10.79% of the time.

The biggest upset was Thorp over Waterville on January 26, 2001. Waterville was a 52.72 point favorite, but lost 32-26.   But that Thorp Waterville upset is suspect since Waterville may have counted it as JV and not brought their best team.  Waterville had too many games: two late season non-league games (including that one) do not appear in their State Tournament program game listing.  (Disclaimer added after initial post).

Upsets with TB Points differences over 30 are quite rare. Of 15,487 such games only eight times was an upset recorded. Better odds than PowerBall, though.

For playoff games, the percentage of upsets is higher, 19.80% of 14,968 games. But that is mostly due to the worst teams getting eliminated quickly, leaving more closely matched games as the tournaments progress. For instance, for all games 79.43% of the games have TB Points rating differences of 26 points or less. For Playoff games 92.38% games are under 26 points. To get under 80% for playoff games, the TB Point margin drops to 17. The actual rate of upsets for various margins is roughly equivalent. The biggest playoff upset was Lopez over Neah Bay on February 18, 2011. Lopez, a 24.88 point underdog won 45-33.

Oh yeah. Hazen was a 22.8 point underdog when it beat Holy Names in 1999: the fifth biggest girls’ playoff upset in my database.

Following is the summary for upsets in games based on the differences of end-of-season TB points rating (so it may not agree with a similar analysis taken of differences during the season). The first column indicates the difference between the two teams point rating (stated in absolute terms). One can see in the last column the percentage of games where the lower rated won steadily decreases, until the ‘rare random event’ portion of the distribution takes over. Girls’ data 1988-2017.

range

All Games

upsets

games

[0,100)

14603

103779

14.07%

[0,1)

1994

4245

46.97%

[1,2)

1899

4195

45.27%

[2,3)

1680

4271

39.34%

[3,4)

1456

4115

35.38%

[4,5)

1270

4041

31.43%

[5,6)

1096

4019

27.27%

[6,7)

963

3937

24.46%

[7,8)

810

3894

20.80%

[8,9)

657

3642

18.04%

[9,10)

560

3745

14.95%

[10,11)

471

3769

12.50%

[11,12)

368

3410

10.79%

[12,13)

294

3323

8.85%

[13,14)

226

3162

7.15%

[14,15)

206

2979

6.92%

[15,16)

155

2874

5.39%

[16,17)

117

2806

4.17%

[17,18)

96

2726

3.52%

[18,19)

88

2513

3.50%

[19,20)

55

2466

2.23%

[20,21)

40

2409

1.66%

[21,22)

23

2133

1.08%

[22,23)

22

2083

1.06%

[23,24)

14

2036

0.69%

[24,25)

15

1811

0.83%

[25,26)

6

1827

0.33%

[26,27)

8

1609

0.50%

[27,28)

4

1471

0.27%

[28,29)

2

1438

0.14%

[29,30)

0

1343

0.00%

[30,31)

1

1282

0.08%

[31,32)

0

1186

0.00%

[32,33)

2

1087

0.18%

[33,34)

0

1068

0.00%

[34,35)

1

960

0.10%

[35,36)

0

903

0.00%

[36,37)

0

844

0.00%

[37,38)

1

678

0.15%

[38,39)

2

696

0.29%

[39,40)

0

591

0.00%

[40,41)

0

618

0.00%

[41,42)

0

495

0.00%

[42,43)

0

550

0.00%

[43,44)

0

482

0.00%

[44,45)

0

430

0.00%

[45,46)

0

380

0.00%

[46,47)

0

341

0.00%

[47,48)

0

297

0.00%

[48,49)

0

287

0.00%

[49,50)

0

252

0.00%

[50,51)

0

244

0.00%

[51,52)

0

195

0.00%

[52,53)

1

179

0.56%

[53,54)

0

160

0.00%

[54,55)

0

140

0.00%

[55,56)

0

106

0.00%

[56,57)

0

116

0.00%

[57,58)

0

102

0.00%

[58,59)

0

91

0.00%

[59,60)

0

99

0.00%

[60,61)

0

111

0.00%

[61,62)

0

67

0.00%

[62,63)

0

72

0.00%

[63,64)

0

47

0.00%

[64,65)

0

55

0.00%

[65,66)

0

42

0.00%

[66,67)

0

28

0.00%

[67,68)

0

27

0.00%

[68,69)

0

26

0.00%

[69,70)

0

23

0.00%

[70,71)

0

20

0.00%

[71,72)

0

17

0.00%

[72,73)

0

17

0.00%

[73,74)

0

4

0.00%

[74,75)

0

14

0.00%

[75,76)

0

11

0.00%

[76,77)

0

10

0.00%

[77,78)

0

7

0.00%

[78,79)

0

4

0.00%

[79,80)

0

4

0.00%

[80,81)

0

2

0.00%

[81,82)

0

4

0.00%

[82,83)

0

2

0.00%

[83,84)

0

3

0.00%

[84,85)

0

2

0.00%

[85,86)

0

0

0.00%

[86,87)

0

0

0.00%

[87,88)

0

3

0.00%

[88,89)

0

0

0.00%

[89,90)

0

0

0.00%

[90,91)

0

3

0.00%

[91,92)

0

1

0.00%

[92,93)

0

0

0.00%

[93,94)

0

0

0.00%

[94,95)

0

1

0.00%

[95,96)

0

0

0.00%

[96,97)

0

1

0.00%

[97,98)

0

0

0.00%

[98,99)

0

0

0.00%

[99,100)

0

0

0.00%

Ranking the rankings: WIAA RPI finishes last

The updated final (I think) ratings for various rankings is done.  Of the eight rankings, the one used to seed the tournament was the very worst at actually picking winners of the games.  It isn’t as easy picking winners are one might think.  You should try it next year.  Rank 16 teams for all twelve tournaments.  But it looks like it is fairly easy to pick winners better than the WIAA RPI did.  Even good old ‘won-loss’ managed to have a higher portion of correct game picks than WIAA RPI.

I don’t know what WIAA has up its sleeve for tournament format.  Don’t know who will be the official ball of the 2018 tournaments.  But I sure hope that the ranking method for seeding gets some rework.  Four of the girls championship games were a repeat of a regional game.  Three of the results switched.  How’s that for making sure the top teams don’t meet until the championship?  At least the regional games weren’t loser out, although the three teams that won the regional and lost the championship are probably wanting a rubber game.

2017 Ratings Comparison

Back by popular demand. Well, I wanted to do it anyway, here is the ranking comparison prior to the state tournaments. This year I’ve collected eight rankings:

WIAA RPI

TeamBrunnhilde RPI

Win-loss

Captured Win-Loss

TeamBrunnhilde Points rating

MaxPreps Rating

Evans Rating

Score Czar rating

The two RPI ratings differ in that TeamBrunnhilde RPI uses available out-of-state records and also utilizes playoff results. The latter three ratings use proprietary rating methods. Win-loss and Captured Win-loss are straightforward calculations one could compute by hand. TeamBrunnhilde Points uses a standard statistical technique.

There is consensus for Boys 3A (Nathan Hale), Girls 1A (Cashmere), and Boys 2B (Kittitas). The other classifications have varying degrees of disagreement between the ratings. While some ratings will have better results than others, all ratings will have notable successes and failures. Like last year, I expect that some ‘sure thing’ game picks (where all rankings agree) will be wrong. The failure rate last year was about 18% for these.

I note that this will be the final year for Score Czar doing high school sports. We’ll miss you. Good luck with your further rating endeavors.