Wichita State Shockers
2026 Team Stats (35 games)
78.2
PPG
70.8
Opp
+7.4
Margin
44.2%
FG%
34.0%
3P%
69.3%
FT%
41.6
RPG
11.5
APG
10.3
TO
82.8
Pace
76.8
AdjO
67.6
AdjD
#79
Rank
Model Outputs
2025-2026
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 1208 (#42) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1101 (#90) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 1153 (#15) | HCA +109 elo |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 993 (#470) | HCA +109 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +9.1 (#79) | HCA +2.2 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +25.2 (#51) | HCA +2.5 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -29.9 (#677) | HCA +2.5 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.735 (#53) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.946 (#28) | NetEff +26.1 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.802 (#79) | AdjNet +12.1 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.804 (#79) | AdjNet +12.2 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More → | 0.698 (#79) | AdjO 76.8 | AdjD 67.6 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.710 (#37) | AdjO 76.7 | AdjD 66.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.390 (#634) | AdjO 71.7 | AdjD 76.6 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.902 (#50) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.149 (#627) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.884 (#45) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.189 (#635) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1251 (#42) | RD 98 | GP 35 |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 849 (#617) | RD 350 | GP 2 |
2026 Schedule & Results
2026 Roster
Minutes by Position
The surface stays filled across the five on-court roles. Use the labels or legend to isolate how each player absorbs guard-to-big minutes.
| Player | Pos | GP | MIN | PTS | REB | AST | STL | BLK | TO | FGA | Numbers | PM | PM/G | PM/40 | FG% | 3P% | FT% | RAPM | TS% | eFG% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
K. Giles
|
- | 35 | 36.3 | 19.3 | 2.6 | 1.4 | 1.6 | 0.0 | 1.3 | 16.9 | 6.8 | 128 | 5.6 | 7.1 | 41.4 | 38.2 | 83.1 | 0.9 | 54.1 | 51.8 |
K. Boyd
|
F | 35 | 33.7 | 10.9 | 5.7 | 1.6 | 0.7 | 0.5 | 1.5 | 9.1 | 8.7 | 139 | 6.0 | 8.0 | 42.3 | 34.6 | 63.8 | 0.89 | 52.2 | 49.2 |
W. Berg
|
C | 34 | 21.8 | 8.9 | 8.2 | 0.7 | 0.4 | 1.2 | 1.0 | 5.4 | 13.0 | 137 | 6.2 | 12.6 | 55.7 | 0.0 | 69.4 | 1.02 | 61.7 | 55.7 |
Tj Williams
|
- | 34 | 21.6 | 8.4 | 4.9 | 1.5 | 0.7 | 0.2 | 1.3 | 6.6 | 7.9 | 142 | 6.5 | 12.6 | 46.0 | 10.0 | 68.4 | 1.12 | 52.1 | 46.2 |
M. Gray Jr.
|
G | 35 | 26.7 | 8.0 | 3.3 | 2.3 | 0.6 | 0.1 | 1.3 | 7.7 | 5.3 | 312 | 14.2 | 27.4 | 33.2 | 30.4 | 85.0 | 2.37 | 47.1 | 42.3 |
D. Battie
|
F | 30 | 17.9 | 7.8 | 4.8 | 0.8 | 0.7 | 0.4 | 0.9 | 5.1 | 8.6 | 10 | 0.5 | 1.6 | 55.9 | 18.8 | 61.4 | -0.01 | 59.8 | 56.9 |
E. Okorafor
|
- | 35 | 17.0 | 6.4 | 5.3 | 0.5 | 0.4 | 1.0 | 1.1 | 4.5 | 7.9 | -25 | -1.1 | -2.9 | 57.0 | 0 | 68.2 | -0.29 | 60.1 | 57.0 |
Dre Kindell
|
- | 35 | 17.2 | 5.8 | 1.5 | 2.3 | 0.7 | 0.1 | 1.1 | 4.3 | 5.1 | 92 | 4.0 | 9.0 | 45.3 | 32.4 | 78.6 | 0.73 | 55.9 | 49.0 |
J. Valencia
|
- | 10 | 14.2 | 3.1 | 1.9 | 0.7 | 0.1 | 0.5 | 0.4 | 3.1 | 2.8 | -15 | -1.4 | -4.6 | 29.0 | 27.3 | 58.8 | -0.19 | 40.3 | 33.9 |
B. Amuneke
|
G | 35 | 7.8 | 2.3 | 0.7 | 0.3 | 0.3 | 0.1 | 0.2 | 2.4 | 1.2 | -14 | -0.6 | -3.4 | 33.3 | 33.3 | 50.0 | -0.19 | 45.0 | 44.0 |
Noah Hill
|
- | 22 | 4.0 | 1.4 | 2.2 | 0.0 | 0.1 | 0.2 | 0.2 | 0.9 | 2.8 | 19 | 1.2 | 10.4 | 65.0 | 0 | 28.6 | 0.08 | 57.3 | 65.0 |
H. Thengvall
|
- | 10 | 2.7 | 0.3 | 0.1 | 0.3 | 0.1 | 0.0 | 0.2 | 0.8 | -0.2 | 22 | 3.1 | 76.7 | 12.5 | 0.0 | 50.0 | 0.21 | 16.9 | 12.5 |
Joy Ighovodja
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Tyrus Rathan-Mayes
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Pierre Couisnard
|
G | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Numbers/Game vs RAPM
X-axis = Numbers/Game (PTS+REB+AST+STL+BLK-TO-FGA), Y-axis = RAPM.
Advanced: Numbers = PTS+REB+AST+STL+BLK-TO-FGA, PM = total +/-, PM/G = per game, PM/40 = per 40 minutes, RAPM = Regularized Adj Plus-Minus, TS% = True Shooting, eFG% = Effective FG