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Banger Environment Index

Expected hits vs results, Saturday, Oct 3

Every skater playing tonight, ranked by expected hits. A player's own hit rate and ice time drive the number. The Banger Environment Index then nudges it for how often tonight's opponent absorbs hits and for home ice. Use it to stream hits in banger leagues and to compare peripheral lines.

13 games · 468 skaters · updated Oct 4, 9:21 AM ET · model bangers-1.1.0

How the projections did (13 finished games): 468 skaters were projected for 555 hits, and those who dressed recorded 554. 29 projected skaters did not dress (scratched or a late lineup change). Mean absolute error per projected skater was 0.94 (0.91 for those who dressed; season benchmarks 0.87 and 0.86).

Game environments for hits

Hits absorbed is how often the opponent gets hit compared with an average team. The environment index adds home ice and the arena factor. Above 1.00 is a better hitting spot than average.

Tonight's games: each team's Banger Environment Index for hits (1.00 = league average) and expected team total.
TeamOpponentOpp. hits absorbedEnv. indexExp. team hitsActual
CHI7:00 PM ET@ BUF1.131.0818.318
BUFvs CHI1.071.0723.029
OTT7:00 PM ET@ TOR0.930.9322.329
TORvs OTT1.101.1023.918
WSH7:00 PM ET@ TBL1.131.0721.324
TBLvs WSH0.920.9621.130
CAR7:00 PM ET@ PHI1.071.0325.913
PHIvs CAR1.221.1829.929
MTL7:00 PM ET@ PIT1.091.0524.928
PITvs MTL1.131.1222.69
UTA7:00 PM ET@ CBJ0.920.9221.026
CBJvs UTA1.061.0621.021
SEA7:00 PM ET@ EDM0.980.9618.418
EDMvs SEA0.950.9821.731
NJD7:30 PM ET@ NYI0.980.9718.015
NYIvs NJD0.950.9820.025
DAL8:00 PM ET@ NSH0.980.9619.829
NSHvs DAL1.131.1121.921
BOS8:00 PM ET@ MIN0.990.9722.716
MINvs BOS1.081.0824.814
STL9:00 PM ET@ COL1.091.0521.528
COLvs STL0.910.9518.716
CGY10:00 PM ET@ VAN0.970.9619.028
VANvs CGY0.940.9720.234
LAK10:00 PM ET@ SJS0.950.9420.719
SJSvs LAK0.971.0021.923

Top forwards by expected hits

Forwards ranked by expected hits on Saturday, Oct 3
#PlayerGameExp. TOIHits/60Env.Exp. hitsP(3+ hits)Actual
1Yakov TreninFMIN vs BOS12:1522.21.084.8279%4
2Kiefer SherwoodFSJS vs LAK15:3816.41.004.2072%5
3Jacob MelansonFSEA @ EDM8:4828.40.963.9669%dnp
4Cole SmithFCHI @ BUF13:0915.11.083.5163%3
5Adam KlapkaFCGY @ VAN11:0519.60.963.4261%4
6Nicolas DeslauriersFCAR @ PHI7:4825.61.033.3961%dnp
7Vasily PodkolzinFEDM vs SEA17:2211.90.983.3460%6
8Colton DachFEDM vs SEA11:3217.70.983.3160%4
9Marcus FolignoFMIN vs BOS11:2616.21.083.3059%1
10Martin PospisilFCGY @ VAN10:4319.20.963.2459%4
11Jordan StaalFCAR @ PHI16:4711.41.033.2459%1
12Ross JohnstonFSTL @ COL10:1118.41.053.2158%dnp
13Carl GrundstromFPHI vs CAR11:0514.91.183.2158%5
14Alex LaferriereFLAK @ SJS19:4010.50.943.2058%3
15Mathieu OlivierFCBJ vs UTA12:5313.51.063.0555%4
16Lawson CrouseFUTA @ CBJ19:0410.40.923.0154%1
17Owen TippettFPHI vs CAR16:239.31.182.9854%3
18Beck MalenstynFBUF vs CHI9:3317.71.072.9754%6
19William CarrierFCAR @ PHI9:3017.91.032.8952%4
20Andrei SvechnikovFCAR @ PHI17:429.51.032.8752%1
21Jack McBainFUTA @ CBJ12:0115.60.922.8551%6
22Tanner JeannotFBOS @ MIN11:3715.30.972.8551%1
23Michael McCarronFMIN vs BOS14:2910.71.082.7549%2
24Dylan CozensFOTT @ TOR18:329.40.932.6748%2
25Jeffrey VielFTBL vs WSH11:0414.70.962.5645%8

Top defensemen by expected hits

Defensemen ranked by expected hits on Saturday, Oct 3
#PlayerGameExp. TOIHits/60Env.Exp. hitsP(3+ hits)Actual
1Connor CliftonDBOS @ MIN18:0611.00.973.1757%0
2Simon BenoitDPHI vs CAR16:518.81.182.8852%dnp
3Nikita ZadorovDBOS @ MIN20:487.40.972.4743%3
4Lian BichselDDAL @ NSH16:269.40.962.4543%5
5Vincent DesharnaisDWSH @ TBL20:496.41.072.3641%4
6Sean WalkerDCAR @ PHI21:016.51.032.3440%0
7Luke SchennDVAN vs CGY12:4511.50.972.3340%3
8Arber XhekajDMTL @ PIT8:3815.11.052.2438%2
9Brenden DillonDNJD @ NYI17:387.70.972.1736%2
10MacKenzie WeegarDUTA @ CBJ21:166.70.922.1536%2
11Mattias SamuelssonDBUF vs CHI22:025.41.072.0834%2
12Jacob TroubaDSJS vs LAK23:315.41.002.0634%2
13Tyler KlevenDOTT @ TOR19:046.50.931.9130%3
14Jayden StrubleDMTL @ PIT11:489.41.051.9130%2
15Elias PetterssonDVAN vs CGY15:317.50.971.8729%1

How the projections work

Player rate
Hits per 60 minutes over all situations. Recent games count more (half-life 40 games), and last season carries in at 50% weight. The rate is shrunk toward the forward or defense average with 25 minutes of league-average ice time, so a five-game heater barely moves it.
Expected ice time
A recency-weighted average of his last games (each game counts 0.8× the one after it). The expected lineup is the 18 skaters who dressed in the team's latest game this season. Anyone who has since moved to another club is dropped, and the gaps are filled from the previous game, then from the club's current roster (call-ups, new arrivals, season openers). A debut with no history gets his position's average rate and a depth-role 12 minutes (forward) or 17 minutes (defenseman). We don't read announced lines, so a late scratch still shows.
Opponent: hits absorbed
Teams that carry the puck more get hit more. Each team's hits absorbed per game (recency-weighted, shrunk 10 games toward league average) becomes an index where 1.00 is average, raised to the power 0.75 (fitted on 2024-25). Tonight's indices run from about 0.89 to 1.06.
Venue and arena
Home skaters were credited 4% more hits than road skaters in 2024-25, and the gap held in 2025-26. Arena scorer bias used to be much bigger. It shrank sharply when the league centralised event recording in 2024-25 (factors within about ±6%) and was undetectable in 2025-26. See the arena pages.
How accurate is it?
Fitted on 2024-25 and tested on every skater-game of 2025-26 (46,636 games), with each projection made before its game. Poisson deviance: model 1.176 vs 1.201 for the player's season-to-date average per game (2.1% better), and mean absolute error 0.856 vs 0.861. Single games are noisy, so the gain is modest. The opponent effect shows up more clearly at team level: the correlation between forecast and actual team hits rises from 0.33 (own rate only) to 0.40 once the opponent is included. These numbers assume the skater dresses. With our lineup rule, 6% of projected skaters (1.1 per team) did not dress in 2025-26. Counting those as zero hits, the deviance is 1.245 and the MAE 0.867, and team totals land within 1.9% of actual.
Probabilities
P(3+ hits) comes from a negative binomial around the expectation (size 7.8, fitted on 2024-25). It is calibrated: in 2025-26, games we gave about 35% landed 38% of the time, and games we gave about 55% landed 59%.

Back-to-backs moved hits by under 1% in both test seasons, and the pregame strength gap (a stand-in for expected score state) by 0–2%, so both are left out. Projections refresh several times a day, and lineups come from each team's recent games, not tonight's announced lines.

FAQ

How are expected hits calculated?

Expected hits = the player's hits per 60 minutes (recency-weighted and shrunk toward his position's average) × his expected ice time × the Banger Environment Index for tonight's opponent and venue. An index of 1.05 means a 5% better than average spot.

What is the Banger Environment Index?

A multiplier for how hit-friendly tonight is: how often the opponent absorbs hits (puck-carrying teams get hit more), times a small home-ice factor, times the arena scorer factor. 1.00 is a league-average spot.

Does the arena still matter for hits and blocks?

Much less than it used to. Up to 2023-24 some rinks credited 40% more hits than others. When the NHL centralised event recording in 2024-25 the effect shrank to within about ±6%, and in 2025-26 it did not repeat from one half-season to the next at all, so the factor is 1.00 everywhere. One more season will show whether it is gone for good. The arena pages show the full history.

Are these betting picks?

No. They are projections for fantasy hockey: an expectation and a probability, with the method and the error rate published. They are built from public NHL box scores and play-by-play.