Edgehalla
Pavol Regenda headshotSan Jose Sharks logo

San Jose Sharks

Pavol Regenda

#84Left Wing26 years oldShoots L6'3"216 lb

0.0 in 2 days
2025-26 regular season
24
GP
9
G
1
A
10
P
42
SOG
21.4% SH
13:21
TOI/GP

LW · SJS · NHL id 8483630

Pavol Regenda has played 24 games for the San Jose Sharks in 2025-26, scoring 9 goals and 1 assist (10 points) on 42 shots while averaging 13:21 a night (11:25 at even strength, 1:28 on the power play and 0:09 shorthanded). At 5v5 he has skated 38% of his 5v5 minutes with Dmitry Orlov and 34% with Kiefer Sherwood over the last 10 games, and the Sharks have controlled 50% of shot attempts with Regenda on the ice (13-16 in goals, 39% offensive-zone starts). In the Sharks' most recent game against WPG he was on L4 with Barclay Goodrow and Zack Ostapchuk and off the power play; dropped to L4 (from L3) in the last game. He is shooting 21.4% this season against 15.2% across the 43 games in our data.

Right now

From the Apr 16 game vs WPG, detected from shift data

Power play
No PP unit
Penalty kill
No PK unit
Dropped to L4 (from L3) in the last gameLost his PP2 spot in the last gameLast 5: L3 · L3/PP2 · L2/PP2 · L3 · L4

Deployment timeline

2025-26 · per-game ice time by strength, with line and power-play unit changes

Usage: 6 games, 12:33 average time on ice, PP1 in 0 games, 1 line changes.

PPLine23▼3▲2340m3m7m10m13m17mMar 12Mar 15Mar 19Mar 24Mar 28Apr 1Apr 4Apr 8Apr 11Apr 15
  • Even strength
  • Power play
  • Penalty kill
  • 5-game average
  • Did not play
  • ▲ promoted · ▼ demoted
6 games shown, 12:33 average. Line and PP unit come from where the player's ice time ranks among his teammates that night.

NHL EDGE tracking

2025-26 percentiles among skaters at his position

Source: NHL EDGE
Top speed39th4022.1 mph
Bursts 20+ mph33rd3431
Hardest shot30th3082.0 mph
Offensive-zone time52nd5342.3%
Distance skated28th2850.5 mi
High-danger SOG44th4424
Percentile among position peers│ tick = 3-season weighted

Season-level NHL EDGE puck and player tracking, refreshed nightly. The percentile is the league rank among skaters this season; the label at the right is the raw value.

Shot map

2025-26 · 66 attempts, 42 on goal, 9 goals

Shot map, OPP at SJS: 66 attempts, 42 on goal, 9 goals, 6.05 expected goals; best chance Pavol Regenda at 0.30 xG.

  • Even strength
  • Power play
  • Penalty kill
  • Goal
  • Shot on goal
  • Missed
  • Blocked
Every attempt (goals, shots, misses and blocks), attacking toward the right (or the top on phones).

Luck

2025-26 · 24 GP · 10 points vs 10.5 expected

Luck Meter →
Hold

Last 20 games: -28. Positive means running hot (sell), negative running cold (buy).

21.4%
97.8
45.5%
G vs xG
9 / 6.0
  • • Role shrank over the last 10 games (TOI down 2:07 a game, PP share 27% → 14%, 2 demotion events): the cold spell may not be all luck.
  • • Shot diet worsened: 0.081 xG per attempt lately vs 0.106 before.
Goals against expected goals
02468+3.0goals above expected1.2σ12/14/16
  • Actual goals
  • Expected
  • ±1σ
  • Above expected
  • Below expected

2025-26 regular season. Rank is among all qualified players; the bar is his percentile among forwards. Each stat links to its explainer.

Fantasy box score

  • Games played24
  • Goals9320th
  • Assists1701st
  • Points10563rd
  • Power-play points4286th
  • Shorthanded points0227th
  • Shots on goal42582nd
  • Hits59342nd
  • Blocked shots6703rd
  • Plus/minus−4420th
  • Penalty minutes20442nd
  • Faceoffs won11305th

Possession & shot quality (5-on-5 on-ice)

Transition, turnovers & faceoffs

Usage

Special teams & discipline

Luck & trend

NHL EDGE tracking

Unranked rows are below the sample minimum (20 GP, 200 five-on-five minutes, 100 draws or 10 goalie games).

Beyond the box score

2025-26 regular season · 23 of 24 games through the sequence engine (more fill in as games are reprocessed)

MethodLeaders
−0.027
Season net
−0.12xG

5-on-5. Each takeaway is credited, and each giveaway charged, at the league xG value of the zone.

MethodLeaders
Per 60
1.10
Attempts
5
0.104

5-on-5 shots within 10 seconds of winning the puck outside the offensive zone.

MethodLeaders
Season
−1.02G
Per 60
−0.191
Drawn / taken
5 / 10minors

MethodLeaders
51.8%
48.7%
52.0%
Transition CF/60
5.7
Turnovers won/60
24.5
Turnovers lost/60
19.9

5-on-5 while he is on the ice.

MethodLeaders
  • More of his shots than the league
  • Fewer
42 shots on goal, 9 goals, all situations
AreaHis shareLeagueG
Low Slot50%23%5
Crease12%3%3
R Circle5%12%0
High Slot0%7%0
Center Point2%9%0
L Circle19%13%1
R Point0%6%0

How his goals happened

9 goals since 2023-24 with NHL EDGE tracking: press Replay to watch the tracking animation

Method
Goal anatomy
GamePlayEntryPassesShot mphNotesReplay
Mar 17, 2026 @ EDMDeflectioncarry262.4deflection
Jan 23, 2026 vs NYRDump and chasedump036.04-on-2
Jan 15, 2026 @ WSHCycle—08.3—
Jan 6, 2026 vs CBJOdd-man rushcarry039.12-on-1
Jan 3, 2026 vs TBLRebounddump031.33-on-2, rebound
Jan 3, 2026 vs TBLRebound—010.2rebound
Jan 3, 2026 vs TBLDeflectioncarry035.52-on-1, rebound
Dec 3, 2025 vs WSHDeflectioncarry125.04-on-3, deflection
Dec 1, 2025 vs UTADeflection—016.7—

* includes a royal-road (cross-slot) pass. Derived from NHL EDGE puck and player tracking.

Linemates

2025-26 · 5v5 time together and how each pairing performs with and without Regenda

TeammatePosGPShareCF% togetherGF% togetherCF% Regenda apartCF% mate apart
Dmitry OrlovD24102:0237%56.3%60% (6-4)46.2% 171:4952.4% 304:56
Mario FerraroD2495:1135%48.8%40% (4-6)50.1% 178:4046.1% 321:13
Alexander WennbergC1291:1733%54.7%57% (4-3)46.6% 60:1733.9% 71:23
Sam DickinsonD2384:5831%46.3%25% (2-6)50.3% 174:4949.7% 267:49
Tyler ToffoliRW874:2127%52.2%50% (3-3)44.2% 23:1730.0% 16:35
John KlingbergD1759:4622%59.6%83% (5-1)45.4% 134:4448.7% 218:36
Timothy LiljegrenD1557:5421%56.6%60% (3-2)46.6% 111:5448.0% 189:30
Vincent DesharnaisD1348:5918%44.3%43% (3-4)53.7% 101:2253.8% 144:43
Michael MisaC847:3217%47.4%14% (1-6)49.0% 48:1550.0% 46:11
Barclay GoodrowC742:2916%52.5%17% (1-5)40.0% 22:5660.5% 22:13
Kiefer SherwoodLW538:1714%51.6%43% (3-4)53.1% 24:0669.9% 37:15
Shakir MukhamadullinD1035:1413%32.7%0% (0-3)56.2% 74:2352.6% 127:32
Zack OstapchukC730:3411%48.1%25% (1-3)32.9% 44:5551.6% 36:16
Vincent IorioD830:0511%49.2%60% (3-2)50.5% 58:5641.4% 93:34

Game log

24 games in 2025-26, newest first

DateOppResultGAP+/-PIMHITBLKEVPPPK
Apr 16@ WPGW 6-1000-101030—9:009:00——
Apr 9@ ANAL 1-6000-20040—11:3211:32——
Mar 24@ NSHL 3-6000-3203050%13:4412:570:47—
Mar 19vs BUFL 0-5000-10030100%14:1612:461:30—
Mar 17@ EDML 3-5101+34061—14:2612:470:04—
Mar 15@ OTTL 4-7000-21031—14:2213:050:52—
Mar 1vs WPGW 2-1000-10000—7:467:46——
Feb 28vs EDMW 5-400001011—10:4810:48——
Feb 2@ CHIL 3-600001250100%11:238:372:140:32
Jan 29@ EDMOTL 3-4000010200%13:5212:241:28—
Jan 27@ VANW 5-2000+11010—14:5013:000:580:52
Jan 23vs NYRW 3-11010302050%18:5512:055:130:24
Jan 20@ TBLL 1-4000-11000—11:5611:56——
Jan 19@ FLAW 4-1000+21430—13:2110:521:270:28
Jan 16@ DETL 2-4000+13010100%18:0115:450:50—
Jan 15@ WSHW 3-2112+21020—13:4611:222:08—
Jan 11vs VGKL 2-7000-10020—12:339:532:32—
Jan 10vs DALW 5-4000-130300%17:0414:042:55—
Jan 7@ LAKW 4-3000+1204150%15:2913:121:30—
Jan 6vs CBJW 5-210103051—14:0511:421:250:47

Season splits by strength

2025-26 regular season · on-ice numbers from shift and play-by-play data

StrengthGPTOI/GPSAGFGAGF%
All situations24320:2213:2132127453.9%51.6%155140222250%17.814.544%
Even strength24274:0511:2525125449.7%47.9%124126131645%12.611.639%
5v524273:5111:2525125449.7%47.9%124126131645%12.611.639%
Power play1935:171:5157493.4%90.7%27370100%3.60.273%
Penalty kill63:370:36050.0%0.0%04030%0.00.60%
5v41935:171:5157493.4%90.7%27370100%3.60.273%
4v563:370:36050.0%0.0%04030%0.00.60%
6v5, own net empty54:210:5212285.7%77.8%321233%0.61.3100%
5v6, opponent net empty73:020:261910.0%12.5%151150%0.90.9—

Career by season

Seasons in Edgehalla's data (2025-26 is being backfilled; earlier seasons follow)

SeasonTeamGPGAP+/-PIMHITBLKTOI/GP
2025-26SJS249110-4204221.4%59613:21
2023-24ANA5000-1270.0%11511:34
2022-23ANA14123-34175.9%81310:33

Pavol Regenda news

Headlines via LineupExperts. Links open the original publisher.

On-ice and deployment numbers are derived by Edgehalla from NHL play-by-play and shift data. Tracking percentiles are from NHL EDGE. Data updates after every game.