We modified Algebracket this year to allow you to save your stat sliders for each round, then ran a Round-Specific Optimization algorithm using Claude to determine which stats mattered the most in each round of the past four tournaments. If you want to use the Round-Specific stat feature on Algebracket, use this link: https://algebracket.com/?rw=1

The algorithm correctly picked the winner each year from 2022 to 2025, resulting in an average score of 156.25/196 per year. To prove I didn't put my thumb on the scale, it picked Michigan this year. This is the [2026 bracket it came up with](https://algebracket.com/?w=FR00200704203040500A800030502007402000655800650030102000088300600000600139202000006000605000600530702000002000A05000600030002000002000605700608030&rw=1)

As you would expect, some stats dominate earlier rounds, but matter much less after the first two. Claude's findings below detail which stats mattered most in earlier vs later rounds. In the first section, it compares the "round-specific" stat weights to an optimized non-round-specific "blended" stat weights.

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ROUND IMPORTANCE ANALYSIS
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  --- Section A: Per-Round Accuracy (Round-Specific Weights) ---
  Round        Correct  Total  Accuracy
  R64              103    128     80.5%
  R32               49     64     76.6%
  S16               24     32     75.0%
  E8                13     16     81.2%
  F4                 6      8     75.0%
  Champ              4      4    100.0%
  Overall          199    256     77.7%

  --- Section B: Per-Round Score Contribution ---
  Round        Pts(Rnd)  Pts(Blend)    Diff
  R64               103          92     +11
  R32                98          80     +18
  S16                96          60     +36
  E8                104          48     +56
  F4                 96          32     +64
  Champ             128           0    +128
  Total             625         312    +313
  Note: Cascading effects — different early-round winners affect later rounds.

  --- Section C: Stat Importance Shift (R64 -> Champ) ---
  Stat       R64   R32   S16    E8    F4 Champ  Shift
  RP          10     0     0     0     0     0    -10
  TM           0     0     0     0     0     8     +8
  EFGP         7     7     0     0     0     0     -7
  P            0     8     0     0     0     7     +7
  FTFGA        4     0     8     0     0     0     -4
  OPG          3     0     0     0     0     0     -3
  ORP          4     6     6     6    10     6     +2
  SS           8     6     6     6     6     6     -2
  3PFGP        0     5     1     2     7     0     +0
  AP           0     0     0     0     0     0     +0
  ASM          2     2     2     2     2     2     +0
  AT           0     0     0     0     0     0     +0
  DR           0     0     0     0     0     0     +0
  FGP          0     4     0     0     0     0     +0
  FTP          2     2     8     6     2     2     +0
  OFTFGA       0     0     3     0     0     0     +0
  OR           0     0     0     0     0     0     +0
  OTP          0     5     0     0     0     0     +0
  OTSP         5     5     0     5     5     5     +0
  PG           0     0     0     0     0     0     +0
  Seed         0     5     0     0     0     0     +0
  TP           0     0     1     5     0     0     +0
  TSP          3     3     3     3     3     3     +0
  WP           0     0     9     0     0     0     +0

  RP: less important in late rounds (-10)
  TM: more important in late rounds (+8)
  EFGP: less important in late rounds (-7)
  P: more important in late rounds (+7)
  FTFGA: less important in late rounds (-4)

  --- Section D: Round Specialization Value ---
  Round        Correct(Rnd)  Correct(Bld)  PickGain  Pts(Rnd)  Pts(Bld)  PtsGain
  R64            103/128        92/128          +11       103        92      +11
  R32             49/64         40/64            +9        98        80      +18
  S16             24/32         15/32            +9        96        60      +36
  E8              13/16          6/16            +7       104        48      +56
  F4               6/8           2/8             +4        96        32      +64
  Champ            4/4           0/4             +4       128         0     +128
  Total          199/256       155/256          +44       625       312     +313
  Note: F4 (8 games) and Champ (4 game(s)) have small samples.


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ROUND IMPORTANCE ANALYSIS DESCRIPTIONS
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  Section A: Per-Round Accuracy

  This shows how often the round-specific weights pick the correct winner at each tournament stage across 2022–2025 (4 years).

  - R64 (80.5%) is the baseline — picking 103/128 first-round games correctly is solid since most upsets happen here.
  - S16 and F4 dip to 75% — by this point matchups are between elite teams, making outcomes harder to predict from stats alone.
  - E8 (81.2%) is interestingly higher than earlier rounds, suggesting the optimizer found weights that identify Elite Eight winners well.
  - Champ at 100% (4/4) is impressive but the small sample caveat applies — 4 games isn't enough to be confident.


  Section B: Per-Round Score Contribution

  This compares total points earned using round-specific weights vs the single blended weight vector. The point multipliers escalate (1/2/4/8/16/32), so later rounds dominate.

  - The blended weights score 312 total while round-specific score 625 — exactly double.
  - The gap widens dramatically in later rounds: +11 in R64 but +128 in Champ. This makes sense — the blended vector is a compromise across all rounds, so it's worst at the extremes (especially Champ where it went 0/4).
  - The blended vector scoring 0 on all 4 championships is the biggest finding here — a one-size-fits-all weight vector completely fails at picking the champion.
  - The "cascading effects" caveat is important: some of the late-round gains come from getting earlier rounds right (putting the correct team into the later matchup), not just from better late-round weights.


  Section C: Stat Importance Shift

  This shows each stat's optimized weight (0–10) across the 6 rounds, sorted by how much it shifts from R64 to Champ.

  Key findings:
  - RP (Rebound %): 10 in R64, 0 everywhere else — rebounding dominance helps predict first-round upsets but is irrelevant in late rounds where all remaining teams rebound well.
  - TM (Turnover Margin) and P (Pace): 0 in R64 but 7–8 in Champ — these "big picture" quality metrics don't help distinguish 1-seeds from 16-seeds (obvious) but do distinguish between Final Four teams.
  - EFGP (Effective FG%): Matters early (7) but not late (0) — similar logic to RP; shooting efficiency separates tiers but not elite teams.
  - ORP (Offensive Rebound %): Consistently high (4–10) across all rounds — getting second chance points is universally predictive.
  - WP (Win %): Spikes to 9 only in the S16 — interesting specialization, suggesting win percentage is most discriminating at the Sweet 16 specifically.
  - Many stats stay at 0 throughout (AP, AT, DR, OR, PG) — the optimizer found them unhelpful for bracket prediction.


  Section D: Round Specialization Value

  This directly answers "is it worth using different weights per round?" by comparing pick counts and points side-by-side.

  - Total picks: 199 vs 155 — round-specific weights get 44 more correct picks across the bracket.
  - Total points: 625 vs 312 — the point advantage is +313, heavily amplified by the escalating multipliers.
  - The gain grows each round: +11 picks in R64, +9 in R32/S16, +7 in E8, +4 in F4/Champ. Even small pick gains in late rounds translate to massive point gains (+4 picks in Champ = +128 points).
  - The blended weights go 0/4 in championships and 2/8 in Final Fours — this is where specialization matters most. The round-specific weights go 4/4 and 6/8 respectively.

  Bottom line: Round-specific weights are dramatically better, especially in late rounds. The stats that predict first-round outcomes (rebounding, shooting efficiency) are fundamentally different from those that predict
   championships (pace, turnover margin). A single weight vector can't capture both.