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Phoenix Mercury

Season: 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017
Also known as: Phoenix Mercury
Program History

2026 Team Stats (2 games)

97.0
PPG
84.5
Opp
-0.6
Margin
46.9%
FG%
39.7%
3P%
84.5%
FT%
38.0
RPG
23.0
APG
17.5
TO
94.3
Pace

Model Outputs

2025 latest available
Catalog

No materialized model snapshot for 2026 yet, so this section is showing the latest available team-model rows.

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 → 1087 (#5) -
Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → 1085 (#4) -
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +45 elo More → 1037 (#5) HCA +45 elo
Margin Margin Linear team-strength model fit on point differential instead of binary wins. HCA +2.6 More → +8.7 (#6) HCA +2.6
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +2.7 More → +6.2 (#6) HCA +2.7
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 0.585 (#7) -
Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. NetEff +2.5 More → 0.585 (#7) NetEff +2.5
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet +9.0 More → 0.780 (#5) AdjNet +9.0
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet +9.3 More → 0.787 (#5) AdjNet +9.3
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 85.6 | AdjD 78.8 More → 0.651 (#6) AdjO 85.6 | AdjD 78.8
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 82.7 | AdjD 79.9 More → 0.565 (#5) AdjO 82.7 | AdjD 79.9
Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.656 (#5) 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. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.640 (#5) Blend of Elo, BT, Margin, PythLog, PtsOD
Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. RD 67 | GP 57 More → 1073 (#5) RD 67 | GP 57

2026 Schedule & Results

Date Vs/At Opponent Result Score
2026-04-25 vs Chicago Sky W 108 - 104
2026-04-29 vs Japan W 86 - 60
2026-05-09 @ Las Vegas Aces W 99 - 66
2026-05-10 @ Golden State Valkyries L 79 - 95
2026-05-12 vs Minnesota Lynx L 84 - 88
2026-05-15 vs Chicago Sky W 91 - 83
2026-05-19 vs Toronto Tempo L 90 - 98
2026-05-21 vs Los Angeles Sparks L 88 - 97
2026-05-24 @ Atlanta Dream L 80 - 82
2026-05-27 @ New York Liberty L 74 - 84
2026-05-29 @ New York Liberty L 68 - 75
2026-06-01 vs Minnesota Lynx L 77 - 111
2026-06-03 @ Seattle Storm W 72 - 68
2026-06-05 @ Portland Fire W 78 - 72

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%
Kahleah Copper Kahleah Copper G 2 18.5 18.0 2.0 0.5 0.5 0.0 1.0 11.5 8.5 - - - 56.5 58.3 75.0 - 72.7 71.7
K. Dunn K. Dunn - 1 12.0 13.0 3.0 1.0 0.0 0.0 1.0 4.0 12.0 - - - 100.0 100.0 100.0 - 133.2 137.5
K. Williams K. Williams G 2 19.5 12.0 2.0 2.0 1.0 0.0 2.5 9.5 5.0 - - - 42.1 42.9 66.7 - 59.1 57.9
Q. Carter Q. Carter F 1 19.0 10.0 3.0 0.0 2.0 0.0 0.0 3.0 12.0 - - - 66.7 50.0 83.3 - 88.7 83.3
N. Mack N. Mack F 2 17.5 8.5 7.5 0.5 1.5 0.0 0.5 5.0 12.5 - - - 70.0 0 75.0 - 72.3 70.0
Sami Whitcomb Sami Whitcomb G 2 17.0 7.5 2.0 1.0 0.0 0.0 1.5 3.5 5.5 - - - 57.1 66.7 100.0 - 90.1 85.7
D. Bonner D. Bonner F 2 17.5 6.5 3.5 0.5 0.5 0.5 2.0 7.5 2.0 - - - 33.3 16.7 100.0 - 40.9 36.7
A. Thomas A. Thomas F 2 20.0 6.0 4.5 8.5 2.0 0.0 4.0 3.5 13.5 - - - 28.6 0 100.0 - 57.0 28.6
S. Carter S. Carter - 1 11.0 6.0 1.0 0.0 0.0 0.0 0.0 3.0 4.0 - - - 66.7 0 50.0 - 63.0 66.7
J. Alleyne J. Alleyne C 2 9.5 5.0 4.0 2.0 0.0 0.5 1.5 2.0 8.0 - - - 100.0 0 100.0 - 102.5 100.0
S. Ciezki S. Ciezki - 2 8.0 5.0 0.0 3.5 0.0 0.0 1.0 3.0 4.5 - - - 50.0 33.3 100.0 - 68.3 58.3
A. Prechtel A. Prechtel - 2 12.5 4.5 4.0 1.0 0.0 1.5 1.0 4.5 5.5 - - - 22.2 25.0 50.0 - 38.7 33.3
L. Jensen L. Jensen - 2 12.5 4.0 1.0 1.5 0.0 0.0 1.0 2.5 3.0 - - - 40.0 0.0 100.0 - 59.2 40.0
Aisha Sheppard Aisha Sheppard G 2 6.5 2.5 0.5 1.0 0.5 0.0 0.5 1.0 3.0 - - - 50.0 0.0 100.0 - 75.3 50.0
M. Maly M. Maly - 2 15.0 2.0 2.0 0.5 1.0 0.0 0.0 3.5 2.0 - - - 14.3 0.0 100.0 - 25.4 14.3
M. Doogan M. Doogan - 2 6.0 1.0 1.5 0.0 0.0 0.0 0.5 2.0 0.0 - - - 0.0 0.0 100.0 - 20.5 0.0

Numbers/Game vs RAPM

Not enough players with both Numbers/Game and RAPM to plot.

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