Banger Environment Index

Expected hits for Tuesday, Oct 6

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.

9 games · 324 skaters · updated Oct 4, 11:30 AM ET · model bangers-1.1.0

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.
Team	Opponent	Opp. hits absorbed	Env. index	Exp. team hits
NSH	@ TOR	0.95	0.94	18.6
TOR	vs NSH	0.99	1.01	21.9
CAR	@ MTL	1.11	1.06	26.1
MTL	vs CAR	1.22	1.19	28.3
OTT	@ DET	1.01	0.99	23.8
DET	vs OTT	1.09	1.09	21.2
UTA	@ NJD	0.96	0.95	21.7
NJD	vs UTA	1.06	1.06	19.7
NYI	@ NYR	1.00	0.98	20.2
NYR	vs NYI	0.97	1.00	24.1
MIN	@ BUF	1.12	1.07	24.2
BUF	vs MIN	0.98	1.00	21.7
STL	@ CHI	1.08	1.04	21.6
CHI	vs STL	0.90	0.94	16.1
VGK	@ SEA	0.97	0.96	23.5
SEA	vs VGK	1.17	1.15	21.8
FLA	@ LAK	0.97	0.96	23.2
LAK	vs FLA	0.99	1.01	22.1

Top forwards by expected hits

Forwards ranked by expected hits on Tuesday, Oct 6
#	Player	Game	Exp. TOI	Hits/60	Env.	Exp. hits	P(3+ hits)
1	Yakov TreninF	MIN @ BUF	13:04	21.9	1.07	5.02	81%
2	Alex LaferriereF	LAK vs FLA	18:48	10.5	1.01	3.30	59%
3	Will CuylleF	NYR vs NYI	14:46	13.5	1.00	3.26	59%
4	William CarrierF	CAR @ MTL	10:24	17.9	1.06	3.24	58%
5	Jordan StaalF	CAR @ MTL	16:44	11.1	1.06	3.23	58%
6	Marcus FolignoF	MIN @ BUF	11:37	15.8	1.07	3.21	58%
7	Jack McBainF	UTA @ NJD	12:46	16.0	0.95	3.19	58%
8	Cole SmithNew team · 3 GP with CHIF	CHI vs STL	13:41	14.9	0.94	3.17	57%
9	Garnet HathawayNew team · 2 GP with FLAF	FLA @ LAK	9:28	20.7	0.96	3.10	56%
10	Keegan KolesarNew team · 1 GP with DETF	DET vs OTT	9:46	17.7	1.09	3.09	56%
11	Marc GatcombNew team · 2 GP with VGKF	VGK @ SEA	9:14	21.1	0.96	3.07	55%
12	Tye KartyeF	NYR vs NYI	14:15	12.6	1.00	2.94	53%
13	Brady TkachukNew team · 2 GP with FLAF	FLA @ LAK	18:39	9.8	0.96	2.88	52%
14	Andrei SvechnikovF	CAR @ MTL	17:44	9.3	1.06	2.88	52%
15	Michael McCarronF	MIN @ BUF	15:13	10.5	1.07	2.81	50%
16	Beck MalenstynF	BUF vs MIN	9:15	18.4	1.00	2.80	50%
17	Lawson CrouseF	UTA @ NJD	17:28	10.2	0.95	2.78	50%
18	Dylan CozensF	OTT @ DET	17:47	9.3	0.99	2.70	48%
19	Joseph VelenoNew team · 3 GP with NYRF	NYR vs NYI	12:31	13.0	1.00	2.67	48%
20	Michael Brandsegg-NygårdF	DET vs OTT	11:32	12.3	1.09	2.55	45%
21	Ivan BarbashevF	VGK @ SEA	15:13	10.4	0.96	2.50	44%
22	Nick FolignoF	MIN @ BUF	12:35	10.8	1.07	2.39	41%
23	Vincent TrocheckNew team · 2 GP with UTAF	UTA @ NJD	18:22	8.3	0.95	2.38	41%
24	Emil HeinemanF	NYI @ NYR	15:08	9.7	0.98	2.37	41%
25	Ross ColtonNew team · 2 GP with NSHF	NSH @ TOR	11:53	12.5	0.94	2.30	39%

Top defensemen by expected hits

Defensemen ranked by expected hits on Tuesday, Oct 6
#	Player	Game	Exp. TOI	Hits/60	Env.	Exp. hits	P(3+ hits)
1	Jeremy LauzonD	VGK @ SEA	16:38	11.6	0.96	3.03	55%
2	Arber XhekajD	MTL vs CAR	10:08	14.7	1.19	2.91	52%
3	Sean WalkerD	CAR @ MTL	21:52	6.3	1.06	2.39	41%
4	Jayden StrubleD	MTL vs CAR	13:05	9.3	1.19	2.37	41%
5	Brenden DillonD	NJD vs UTA	16:20	7.7	1.06	2.21	37%
6	MacKenzie WeegarD	UTA @ NJD	21:00	6.6	0.95	2.17	36%
7	Tyler KlevenD	OTT @ DET	19:57	6.6	0.99	2.15	36%
8	Ben ChiarotD	DET vs OTT	19:37	5.9	1.09	2.08	34%
9	Cale FleuryD	SEA vs VGK	14:15	7.7	1.15	2.06	34%
10	Mattias SamuelssonD	BUF vs MIN	22:40	5.4	1.00	2.00	32%
11	Radko GudasNew team · 2 GP with FLAD	FLA @ LAK	11:56	10.3	0.96	1.94	31%
12	Parker WotherspoonNew team · 2 GP with VGKD	VGK @ SEA	19:54	6.0	0.96	1.88	29%
13	Alexander RomanovD	NYI @ NYR	19:12	5.9	0.98	1.82	28%
14	Braden SchneiderD	NYR vs NYI	18:03	5.7	1.00	1.70	25%
15	K'Andre MillerD	CAR @ MTL	23:37	3.9	1.06	1.60	23%

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.