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Banger Environment Index

Expected hits vs results, Thursday, Oct 1

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.

8 games · 288 skaters · updated Oct 4, 9:20 AM ET · model bangers-1.1.0

How the projections did (8 finished games): 288 skaters were projected for 321 hits, and those who dressed recorded 285. 69 projected skaters did not dress (scratched or a late lineup change). Mean absolute error per projected skater was 0.99 (0.98 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
PHI7:00 PM ET@ NJD0.940.9423.824
NJDvs PHI1.081.0820.118
TBL7:00 PM ET@ NYR0.980.9621.031
NYRvs TBL1.131.1227.022
BUF7:00 PM ET@ CBJ0.920.9219.821
CBJvs BUF1.131.1221.825
MIN8:00 PM ET@ NSH0.980.9622.221
NSHvs MIN0.981.0019.625
SEA9:00 PM ET@ CGY0.940.9417.820
CGYvs SEA0.950.9819.224
CHI9:30 PM ET@ UTA1.081.0417.812
UTAvs CHI1.041.0523.142
EDM10:00 PM ET@ VAN0.970.9621.320
VANvs EDM0.970.9920.428
FLA10:00 PM ET@ SJS0.910.9221.541
SJSvs FLA0.991.0122.124

Top forwards by expected hits

Forwards ranked by expected hits on Thursday, Oct 1
#PlayerGameExp. TOIHits/60Env.Exp. hitsP(3+ hits)Actual
1Yakov TreninFMIN @ NSH12:0022.70.964.3274%2
2Kiefer SherwoodFSJS vs FLA16:2615.71.014.3074%8
3Lawson CrouseFUTA vs CHI20:5910.21.053.6966%4
4Will CuylleFNYR vs TBL15:0113.11.123.6165%3
5Garnet HathawayNew team · 2 GP with FLAFFLA @ SJS10:5420.50.923.3661%3
6Cole SmithNew team · 3 GP with CHIFCHI @ UTA12:4315.21.043.3160%3
7Matt RempeFNYR vs TBL8:4020.71.123.3059%dnp
8Vasily PodkolzinFEDM @ VAN17:0412.20.963.2759%2
9Colton DachFEDM @ VAN11:0218.70.963.2559%0
10Tye KartyeFNYR vs TBL13:3212.71.123.1557%5
11Mathieu OlivierFCBJ vs BUF12:0313.61.123.0154%3
12Carl GrundstromFPHI @ NJD12:1014.90.942.8050%2
13Joseph VelenoNew team · 3 GP with NYRFNYR vs TBL11:0213.51.122.7449%4
14Brady TkachukNew team · 2 GP with FLAFFLA @ SJS18:159.50.922.6146%5
15Alex FormentonFEDM @ VAN15:0910.60.962.5345%1
16Michael CarconeFUTA vs CHI14:459.61.052.4543%6
17Max JonesFEDM @ VAN9:2416.40.962.4242%2
18Michael McCarronFMIN @ NSH15:0210.10.962.4042%6
19Ty DellandreaFSJS vs FLA12:3511.41.012.3741%dnp
20Trent FredericFEDM @ VAN10:2114.40.962.3440%2
21Owen TippettFPHI @ NJD16:319.10.942.3340%4
22Eeli TolvanenNew team · 3 GP with NYRFNYR vs TBL14:318.71.122.3240%0
23Zemgus GirgensonsFTBL @ NYR12:2411.20.962.2137%3
24Paul CotterNew team · 3 GP with VANFVAN vs EDM10:3412.50.992.1636%5
25Kasperi KapanenFEDM @ VAN17:317.80.962.1536%1

Top defensemen by expected hits

Defensemen ranked by expected hits on Thursday, Oct 1
#PlayerGameExp. TOIHits/60Env.Exp. hitsP(3+ hits)Actual
1Brenden DillonDNJD vs PHI19:047.81.082.6547%1
2Simon BenoitNew team · 2 GP with PHIDPHI @ NJD18:158.80.942.4743%2
3MacKenzie WeegarDUTA vs CHI21:356.11.052.2739%7
4Luke SchennNew team · 3 GP with VANDVAN vs EDM12:1211.40.992.2739%3
5Mattias SamuelssonDBUF @ CBJ23:525.60.922.0132%0
6Elias PetterssonDVAN vs EDM15:127.90.991.9631%0
7Braden SchneiderDNYR vs TBL18:305.61.121.9230%2
8Johnathan KovacevicDNJD vs PHI18:525.31.081.7727%dnp
9Erik CernakDTBL @ NYR18:455.80.961.7226%5
10Radko GudasNew team · 2 GP with FLADFLA @ SJS11:169.90.921.6925%4
11Artyom LevshunovDCHI @ UTA21:144.61.041.6824%1
12Jamie OleksiakNew team · 3 GP with VANDVAN vs EDM19:155.10.991.6123%2
13Zach WhitecloudDCGY vs SEA22:064.40.981.5622%2
14Filip HronekDVAN vs EDM26:223.60.991.5421%0
15Nick SeelerDPHI @ NJD17:085.80.941.5421%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.