OPENING DAY FOR THE REGGIE BUSH LEAGUE, Season 7

37 entries here for the analytics

Once again we're running custom analytics for this year's Tournament. Unlike other sites, which keep track of your own scores and scoring opportunities, the Reggie Bush League recognizes that this is essentially a friendly competition, where your absolute score is less important than whether or not you land in the top three. And this means you have to keep track of how everyone else is doing, and whether or not there are others who can block your path to the top.

Thanks to the 34 PDF brackets that arrived before midnight Wednesday

The three entries ("CHRIS-11", "dj toothpic" and "Holland, Like the Country") that didn't bother to send me a PDF, or sent them after the deadline are now playing with Watertown Food Pantry as a partner -- 50% of all your winnings will be sent to your (new) partner! No deposit insurance for you!

HERE ARE THE 2023 ENTRIES, by predicted Tourney champ

In [3]:
_ = printLines(top_picks)
                      CHAMP            RUNNER-UP        HANDLE
36 *** Holland        Alabama          Houston          Holland, Like the Country
 3 Joe Felion         Alabama          Texas            Cinderella
20 Matt Manderfeld    Alabama          Texas            Peck 'em Jays
33 Rob Hamilton       Arizona          Houston          Cocaine Bruin
26 Jennifer Andrade   Arizona          Texas            Derek Harper's Heart
 0 Nathan Rothstein   Arizona          Texas            Bad Bets US Trust
12 Bibeka Shrestha    Baylor           Indiana          bammashamma
21 Wyatt Posig        Gonzaga          San Diego State  y
35 *** DJ Toothpic    Houston          Alabama          dj toothpic
28 Lauren Wanski      Houston          Alabama          Tulane all the way!
 5 Steve Maltzman     Houston          Alabama          Cranky Boeheim
 2 Dave DeBlasio      Houston          Arizona          NC Double Hey
22 Grayson Holland    Houston          Arizona          The Professor
 7 Dan Levine         Houston          Purdue           Dan Levine Bracket
23 Jim Gammill        Kansas State     Gonzaga          I State My Case
 1 Casey Farrell      Kansas           Arizona          C.Farrell
25 Sarah DeBlasio     Kansas           Arizona          Sarah's Bracket 2023
19 Alex Lessin        Kansas           Marquette        Maryland Wild Ass Guess
10 Joel Worthington   Kansas           Purdue           Champion Supernova
 9 Dan Rothstein      Kentucky         Kansas           SabaAbuela
 8 Michael Tracy      Marquette        Gonzaga          Edey's Playhouse
15 Brett Stone        Marquette        Kansas           Silicon Valley BANG! BANG
14 Hampton Barclay    Purdue           Texas            2Cool4NIT
16 Jonathan Graboyes  Purdue           UCLA             Jon Graboyes
27 Andrew Koziol      Purdue           UConn            Meatball Andy AKA Tiny A
31 Kevin Broich       Purdue           UConn            Brioch Bracket
32 Matt Saldana       Texas A&M        Tennessee        Srsly V. Beautiful (SVB)
 4 Paul Mazanec       Texas            Alabama          Lombardhoopz
11 Peter Felion       Texas            Alabama          PF
 6 Griffin Gammill    Texas            Duke             gammajamma
17 Zachary Jonas      Texas            Kentucky         The Other Tim Allen
13 Luke Barclay       UCLA             Arizona          Lil Luke's Big Bracket
18 Ben Sampson        UCLA             Baylor           Run on the Bank Shot
24 Jamie McDonald     UCLA             Purdue           Yeti Life
30 Julia Barclay      Virginia         Houston          j barc
29 Anna Barclay       Virginia         Kansas           anna barc
34 *** Chris          Xavier           Purdue           CHRIS-11

FAVORITE PICKS: here's how we evaluated the field

A collective measure of how far we think each school is going

Across the 36 brackets submitted, this is a count, and average per player, of how many times each school is picked to win a game.

Remember there are six games in all up to the championship game.

Seven teams were not picked at all, not even to win a single game.

Does Duke really deserve its 14th-spot on this list?

In [4]:
_ = printLines(favorites)
 1 ( 1) Houston                   131      3.5
 2 ( 1) Alabama                   125      3.4
 3 ( 2) Texas                     117      3.2
 4 ( 1) Kansas                    117      3.2
 5 ( 1) Purdue                    114      3.1
 6 ( 2) Arizona                   110      3.0
 7 ( 2) Marquette                 109      2.9
 8 ( 2) UCLA                      103      2.8
 9 ( 3) Gonzaga                    97      2.6
10 ( 3) Baylor                     76      2.1
11 ( 4) UConn                      75      2.0
12 ( 4) Virginia                   66      1.8
13 ( 3) Xavier                     65      1.8
14 ( 5) Duke                       64      1.7
15 ( 3) Kansas State               60      1.6
16 ( 4) Tennessee                  59      1.6
17 ( 4) Indiana                    59      1.6
18 ( 6) Kentucky                   57      1.5
19 ( 6) Iowa State                 49      1.3
20 ( 5) San Diego State            40      1.1
21 ( 5) Miami                      40      1.1
22 ( 6) Creighton                  33      0.9
23 ( 7) Michigan State             31      0.8
24 ( 8) Memphis                    29      0.8
25 ( 5) Saint Mary's               29      0.8
26 ( 6) TCU                        29      0.8
27 ( 8) Arkansas                   29      0.8
28 ( 7) Texas A&M                  28      0.8
29 ( 7) Missouri                   26      0.7
30 (10) Boise State                26      0.7
31 ( 8) Maryland                   24      0.6
32 ( 8) Iowa                       23      0.6
33 (12) VCU                        23      0.6
34 (12) Charleston                 20      0.5
35 (10) Penn State                 20      0.5
36 (11) NC State                   19      0.5
37 ( 9) Auburn                     19      0.5
38 (12) Drake                      19      0.5
39 (10) Utah State                 18      0.5
40 ( 9) West Virginia              16      0.4
41 ( 7) Northwestern               16      0.4
42 (11) Arizona State              16      0.4
43 ( 9) Florida Atlantic U         14      0.4
44 (11) Providence                 14      0.4
45 ( 9) Illinois                   14      0.4
46 (11) Pittsburgh                 11      0.3
47 (10) USC                        10      0.3
48 (12) Oral Roberts               10      0.3
49 (13) Furman                      6      0.2
50 (14) UCSB                        6      0.2
51 (13) Louisiana                   4      0.1
52 (13) Kent State                  4      0.1
53 (13) Iona                        4      0.1
54 (14) Kennesaw St                 3      0.1
55 (14) Montana State               2      0.1
56 (15) Vermont                     2      0.1
57 (14) Grand Canyon                1      0.0
58 (15) Princeton                   0      0.0
59 (16) Texas A&M Corpus Christi    0      0.0
60 (16) Fairleigh Dickinson         0      0.0
61 (15) Colgate                     0      0.0
62 (16) N Kentucky                  0      0.0
63 (15) UNC Asheville               0      0.0
64 (16) Howard                      0      0.0

THE UPSET MEASURE: Who likes upsets?

This score is the sum of the seeds for the picked school in each of the 63 games in a bracket.

Note that if you pick the lower seed for every game, your Upset Score would be 203

The Rothstein family continues to hold on to top spots here, year in year out.

In [5]:
_ = printLines(upset_measures)
         Nathan Rothstein    385
            Dan Rothstein    363
              Jim Gammill    309
             Rob Hamilton    309
             Matt Saldana    300
             Peter Felion    293
             Luke Barclay    291
                *** Chris    289
               Dan Levine    288
            Michael Tracy    285
            Zachary Jonas    285
              *** Holland    283
          Griffin Gammill    282
            Andrew Koziol    277
            Lauren Wanski    271
           Steve Maltzman    269
          Bibeka Shrestha    269
              Wyatt Posig    269
         Joel Worthington    266
             Kevin Broich    263
          *** DJ Toothpic    263
        Jonathan Graboyes    261
              Brett Stone    259
            Julia Barclay    259
         Jennifer Andrade    257
             Paul Mazanec    254
          Grayson Holland    254
           Jamie McDonald    254
          Hampton Barclay    244
               Joe Felion    239
              Ben Sampson    238
              Alex Lessin    238
           Sarah DeBlasio    228
             Anna Barclay    227
            Dave DeBlasio    222
          Matt Manderfeld    222
            Casey Farrell    213

And here are the top seeds (8 or higher) picked most often to lose in round 1

In [6]:
_ = printLines(number_upset_picks)
Northwestern         23
Saint Mary's         18
Iowa                 17
Texas A&M            17
Creighton            16
Maryland             16
Missouri             16
Arkansas             14
San Diego State      14
TCU                  14
Miami                13
Memphis              12
Kentucky             11
Iowa State           10
Michigan State       10
Duke                  6
Virginia              5
Baylor                4
Tennessee             4
Indiana               3
UConn                 3
Xavier                3
Kansas State          2
Gonzaga               1
Marquette             1
Alabama               0
Arizona               0
Houston               0
Kansas                0
Purdue                0
Texas                 0
UCLA                  0

WHO ARE YOU LIKE? WHO IS MOST LIKE YOU?

Each player's entry predicts the number of games each school will win. If you multiply your number of wins times another player's prediction, you'll get a big number (well, 36) if you both pick that team as the champ. You will get 0 if either one of you picks the school to go out in the first round -- EXCEPT if you both pick the school to go out in the first round, then you get 1 point for that call.

Now just add up this score for the two players for each school (from 0 to 36) for all the schools, and you get this measure of how much your entry is like someone else's.

The next table shows the highest score for each player, and the player (or players) that match that score.

A high score means your match is more like you than a lower score.

yeah, I know this sounds a bit confusing -- gotta try it out though....

In [7]:
_ = printLines(most_similar)
score   Player            Player(s) most similar
191     Alex Lessin          Sarah DeBlasio      
191     Sarah DeBlasio       Alex Lessin         
189     Paul Mazanec         Hampton Barclay     
189     Hampton Barclay      Paul Mazanec        
187     Casey Farrell        Dave DeBlasio & Sarah DeBlasio
187     Dave DeBlasio        Casey Farrell       
186     Anna Barclay         Dave DeBlasio       
184     Jamie McDonald       Hampton Barclay     
182     Joe Felion           Matt Manderfeld & *** DJ Toothpic
182     Matt Manderfeld      Joe Felion          
182     Andrew Koziol        Kevin Broich        
182     Kevin Broich         Andrew Koziol       
182     *** DJ Toothpic      Joe Felion          
181     Grayson Holland      Casey Farrell       
180     Ben Sampson          Anna Barclay        
180     Jennifer Andrade     Anna Barclay        
179     Zachary Jonas        Paul Mazanec        
179     Lauren Wanski        Alex Lessin         
178     *** Holland          *** DJ Toothpic     
175     Joel Worthington     Casey Farrell       
175     Brett Stone          Casey Farrell & Sarah DeBlasio
174     Dan Levine           Kevin Broich        
174     Peter Felion         Joe Felion          
173     Jonathan Graboyes    Matt Manderfeld     
172     Julia Barclay        Anna Barclay        
170     Steve Maltzman       Matt Manderfeld     
169     Luke Barclay         Anna Barclay        
168     Michael Tracy        Joe Felion          
168     Wyatt Posig          Matt Manderfeld & Jim Gammill
168     Jim Gammill          Wyatt Posig         
166     Griffin Gammill      Steve Maltzman & Zachary Jonas
166     *** Chris            Anna Barclay        
165     Rob Hamilton         Jennifer Andrade    
163     Bibeka Shrestha      Wyatt Posig         
159     Dan Rothstein        Luke Barclay        
158     Matt Saldana         Grayson Holland     
148     Nathan Rothstein     Rob Hamilton        

WHAT MAKES A GOOD BRACKET?

Be different

In the Reggie Bush League, the big payoff is to come in first.

If you've picked Houston to win it all, you have a lot of company -- and you will have to be pretty lucky in the early rounds to win the top prize.

In contrast, if you are the only one who picked a particular school to win, what happens in the early rounds is less inportant, You need your team to make it to the Final Four of course, but after that, your path to winning is a lot less complicated.

But pick favorites

The seeds carry a lot of information about value. Top seeds in general do better than lower seeds. Not all the time of course, but often enough to make me think that a bracket with a lot of upsets is likely to not win.

EXPECTED STARTING RANKINGS -- BEFORE THE GAMES BEGIN

It pays to not pick the Fan Favorites.

This next report captures these two ideas about who has a strong bracket this year -- those who have picked teams that are not favored by a lot of other players but still have a strong seeds.

I created a composite score to capture these factors, and have ranked the entries here.

My Daughter-in-law is Number One! And my lucky Son-in-law is Last!

In [8]:
_ = printLines(starting_positions)
 1 Bibeka Shrestha           4.94 Baylor                 Indiana               
 2 Alex Lessin               4.46 Kansas                 Marquette             
 3 Wyatt Posig               4.42 Gonzaga                San Diego State       
 4 Ben Sampson               3.64 UCLA                   Baylor                
 5 Brett Stone               3.49 Marquette              Kansas                
 6 Matt Manderfeld           3.42 Alabama                Texas                 
 7 Casey Farrell             3.41 Kansas                 Arizona               
 8 Jonathan Graboyes         3.21 Purdue                 UCLA                  
 9 Anna Barclay              3.21 Virginia               Kansas                
10 Kevin Broich              3.17 Purdue                 UConn                 
11 Joel Worthington          3.09 Kansas                 Purdue                
12 *** Chris                 3.03 Xavier                 Purdue                
13 Sarah DeBlasio            2.98 Kansas                 Arizona               
14 *** Holland               2.98 Alabama                Houston               
15 Joe Felion                2.95 Alabama                Texas                 
16 Andrew Koziol             2.85 Purdue                 UConn                 
17 Jamie McDonald            2.77 UCLA                   Purdue                
18 Matt Saldana              2.60 Texas A&M              Tennessee             
19 *** DJ Toothpic           2.58 Houston                Alabama               
20 Dave DeBlasio             2.56 Houston                Arizona               
21 Michael Tracy             2.54 Marquette              Gonzaga               
22 Jim Gammill               2.50 Kansas State           Gonzaga               
23 Steve Maltzman            2.47 Houston                Alabama               
24 Griffin Gammill           2.46 Texas                  Duke                  
25 Hampton Barclay           2.45 Purdue                 Texas                 
26 Lauren Wanski             2.43 Houston                Alabama               
27 Paul Mazanec              2.40 Texas                  Alabama               
28 Julia Barclay             2.18 Virginia               Houston               
29 Dan Levine                2.16 Houston                Purdue                
30 Grayson Holland           1.96 Houston                Arizona               
31 Jennifer Andrade          1.80 Arizona                Texas                 
32 Peter Felion              1.80 Texas                  Alabama               
33 Rob Hamilton              1.77 Arizona                Houston               
34 Zachary Jonas             1.56 Texas                  Kentucky              
35 Luke Barclay              1.49 UCLA                   Arizona               
36 Dan Rothstein             1.45 Kentucky               Kansas                
37 Nathan Rothstein          0.80 Arizona                Texas                 

RANKING BY SIMULATION RUNS

A Simulation Run is doing a "what-if" for all 63 games in the Tourney, keeping track of how many points each player would have scored in that run, and determining who would have come in first, second, and third.

Simulation Analysis does thousands, if not millions, of runs, and averages the results across all the runs. This lets us see the inter-related effects of each other's bracket choices, to see which brackets are more likely to have a path to the top of the scoring.

Some basic numbers

With 63 games, there are 2-to-the-63rd possible paths -- that's 9,223,372,036,854,775,808. So even if we could run a billion paths, that's a pretty small sample of what's possible.

But with 32 games played in the first two days, the number of possible paths given the remaining teams is only 2,147,483,648 -- a much more reasonable number.

And after the first weekend is over, and only 16 teams remain, the number of possible paths shrinks to 32,768. In past years, the heat maps prior to the Sweet 16 look at all 32,768 possible paths.

It's all in the weights

In the first and second rounds, I give strong weight to the seeds. In the third round, I make it more likely a toss-up. And for the Elite 8 forward, I give each game a 50-50 weighting -- I figure if a team has made it that far, it must be pretty good regardless of its initial seed.

I run this for 50,000 simulated paths. Not a whole lot, but enough for today.

The results look somewhat similar to the rankings given above -- the simulations show that those who have brackets with less popular picks are more likely to win. But no one should count their winnings until the games have been played.

Those who've picked Houston, Kansas, Purdue or Texas need to be extra lucky in the early rounds

In [9]:
_ = printLines(expected_values)
                      E(value)   Pr(1st)   Pr(2nd)   Pr(3rd) Avg-Win  Best-Win
 1      Casey Farrell    43.65      4.92      5.84      5.19     1218     1870
 2        Wyatt Posig    41.91      5.36      1.40      1.09     1103     1700
 3    Bibeka Shrestha    40.80      5.09      2.20      1.45     1079     1640
 4        Ben Sampson    40.71      4.55      5.94      3.49     1192     1800
 5    Matt Manderfeld    38.45      4.33      4.91      3.76     1233     1760
 6          *** Chris    37.78      4.82      1.26      1.17     1145     1710
 7        Brett Stone    37.30      4.67      2.85      1.17     1233     1760
 8        Jim Gammill    34.54      4.37      1.63      0.88     1059     1580
 9      Dave DeBlasio    34.06      3.96      4.67      4.56     1240     1770
10   Jennifer Andrade    33.46      4.13      2.56      2.03     1253     1710
11       Kevin Broich    29.36      3.35      3.84      3.53     1171     1740
12         Joe Felion    29.34      3.21      4.98      3.50     1226     1740
13     Jamie McDonald    28.61      3.16      4.08      3.55     1175     1740
14        Alex Lessin    26.17      2.69      4.47      6.53     1222     1750
15  Jonathan Graboyes    25.64      2.87      3.48      3.46     1246     1760
16       Paul Mazanec    25.58      3.10      2.79      2.71     1238     1750
17      Michael Tracy    25.14      2.76      4.78      2.25     1169     1720
18    Hampton Barclay    24.88      2.83      3.22      3.66     1252     1730
19       Anna Barclay    24.69      2.99      2.51      1.64     1181     1760
20     Sarah DeBlasio    22.97      2.35      4.42      4.63     1207     1770
21    Griffin Gammill    22.40      2.65      2.08      2.26     1081     1740
22   Joel Worthington    21.38      2.44      2.37      3.49     1286     1730
23      Andrew Koziol    20.61      2.29      2.99      2.74     1098     1640
24        *** Holland    19.24      2.15      1.84      4.47     1301     1670
25    *** DJ Toothpic    19.13      2.29      2.30      3.38     1262     1710
26       Peter Felion    18.51      2.16      2.58      2.01     1246     1700
27       Matt Saldana    16.48      2.06      0.99      0.73      999     1660
28     Steve Maltzman    15.92      1.67      2.67      2.91     1209     1710
29       Luke Barclay    15.49      1.64      2.28      3.37     1273     1720
30       Rob Hamilton    15.46      1.78      2.32      1.55     1246     1680
31    Grayson Holland    15.10      1.68      2.81      2.48     1170     1700
32      Julia Barclay    14.35      1.51      3.23      1.55     1195     1740
33         Dan Levine    10.97      1.25      1.58      1.86     1301     1680
34      Lauren Wanski     9.84      1.03      2.34      2.53     1218     1720
35      Dan Rothstein     9.68      1.25      0.32      0.28     1093     1490
36      Zachary Jonas     8.41      0.88      1.65      1.76     1100     1710
37   Nathan Rothstein     1.99      0.08      1.07      1.27     1173     1480

Why don't the probabilities add up to 100%? -- it's because of TIES

In the calculation of odds of coming in first, second or third, I give each player "full credit" even if they tie. I figure you can claim bragging rights even if you tie for first place instead of win it outright. This is why the probabilities for first and second sum up to more than 100%. The third place probabilities happen to sum up to less that 100%, because even considering the possible ties for third, if there are ties for either first or second, there is no separate third place winner.

The E(value) calculation however takes ties into account -- if you tie for first, the two of you split 95% of the pool, as opposed to a single first place winner getting 85% and a single second place winner getting 10%.

You will be able to find this page later at jgweb.info/RBL23/RBL23-day-0.html.

You can get back to the current page at jgweb.info.

Earlier Pages

Day 0: jgweb.info/RBL23/RBL23-day-0.html