Edgehalla

Injury cascade

Who benefits when Michael Carcone is out?

Michael Carcone (L, Utah Mammoth) plays 13:26 a night on L4 and PP2, with 22% of the team's power-play time over his last 10 games. He has missed the team's last 2 games.

Based on 3 games without Michael Carcone and 90 with him. Through games of Oct 13, 2026.

Expected change per game without him

Ranked beneficiaries

Show
Players whose ice time and power-play share rose without the injured player, ranked by expected fantasy opportunity.
PlayerGamesExp. Δ TOIRost %Basis
Kailer YamamotoRIn 3 games without Michael Carcone, Kailer Yamamoto played 13:01 (+0:57) and PP share went 6% → 16%.312:04 → 13:01+0:346% → 16%0/33.4%▲3.2Games without him
Mikhail SergachevDIn 3 games without Michael Carcone, Mikhail Sergachev played 24:54 (+0:37) and PP share went 55% → 63%.324:17 → 24:54+0:2255% → 63%3/398%Games without him
Dylan GuentherRIn 3 games without Michael Carcone, Dylan Guenther played 17:33 (+0:05) and PP share went 58% → 67%.317:28 → 17:33+0:0358% → 67%3/399%Games without him
John MarinoDIn 3 games without Michael Carcone, John Marino played 19:34 (−0:53) and PP share went 4% → 18%.320:27 → 19:34−0:324% → 18%0/316%▼0.9Games without him
Vincent TrocheckCIn 2 games without Michael Carcone, Vincent Trocheck played 17:04 (+0:08) and PP share went 21% → 26%.216:56 → 17:04+0:0421% → 26%0/285%▼2.3Games without him
Clayton KellerRIn 3 games without Michael Carcone, Clayton Keller played 18:43 (−0:22) and PP share went 59% → 67%.319:05 → 18:43−0:1359% → 67%3/3100%Games without him

FAQ

Who benefits when Michael Carcone is out?

Kailer Yamamoto projects to gain the most (+0:34 a game). In 3 games without Michael Carcone, Kailer Yamamoto played 13:01 (+0:57) and PP share went 6% → 16%.

How many games has UTA played without Michael Carcone?

3 games in the loaded seasons while he was on the roster, against 90 with him.

How it works

  1. We find every game the injured player missed while on the team's roster this season and last (including the current absence), and compare each teammate's ice time, power-play share and PP1 usage in those games with the games the injured player dressed.
  2. Changes are shrunk toward zero when the sample is small (a change over n games counts n / (n + 2) of its size), so one odd game does not crown a beneficiary.
  3. When there is little or no absence history, we fall back to line adjacency from our shift-based line detection: the next man up on his power-play unit (the top PP2 player at his position) and the best player one line or pair below his.
  4. Players are ranked by expected fantasy opportunity: TOI minutes, plus power-play share (10 points of PP share ≈ 1.2 minutes of even-strength TOI), plus PP1 gains.

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