ICE Hockey League 2026/27 — results, standings and analytics

Scheduled games 40

Win probability, next 12 gamesHome and away segments use each team's colors; gray shows the overtime/shootout share

Table view

Team	Home wins in regulation	Level after regulation	Away wins in regulation	Total
RBS–MHC	34	16	50	100
HCB–FTC	72	12	16	100
OLL–PIV	76	10	14	100
HCP–AVS	71	13	16	100
VSV–VIC	40	17	43	100
BWL–KAC	18	13	69	100
FTC–VIC	35	16	49	100
G99–OLL	39	16	45	100
VSV–HCB	19	14	67	100
AVS–RBS	27	17	56	100
BWL–HCP	26	15	59	100
PIV–KAC	7	7	86	100

Predicted outcomes for scheduled games, sortable by any column

04 Oct 2026, 15:00	RBSvsMHC	2.8 – 3.3	42%	16%	58%	2:3 5%	1.25 – 1.75	Medium
04 Oct 2026, 16:00	HCBvsFTC	4.4 – 2.3	78%	12%	22%	4:2 5%	2.35 – 0.65	Medium
04 Oct 2026, 16:00	OLLvsPIV	4.9 – 2.4	81%	10%	19%	4:2 5%	2.44 – 0.56	Medium
04 Oct 2026, 17:00	HCPvsAVS	3.6 – 1.8	77%	14%	23%	3:1 6%	2.32 – 0.68	Medium
04 Oct 2026, 17:30	VSVvsVIC	2.7 – 2.8	48%	17%	52%	2:2 6%	1.45 – 1.55	Medium
04 Oct 2026, 17:30	BWLvsKAC	2.5 – 4.3	25%	13%	75%	2:4 5%	0.74 – 2.26	Medium
09 Oct 2026, 18:30	FTCvsVIC	2.9 – 3.3	43%	16%	57%	2:3 5%	1.29 – 1.71	Medium
09 Oct 2026, 18:30	G99vsOLL	3.0 – 3.2	47%	16%	53%	3:3 5%	1.40 – 1.60	Medium
09 Oct 2026, 19:15	VSVvsHCB	2.2 – 3.8	26%	14%	74%	2:3 5%	0.79 – 2.21	Medium
09 Oct 2026, 19:15	AVSvsRBS	2.2 – 3.1	35%	17%	65%	2:3 6%	1.06 – 1.94	Medium
09 Oct 2026, 19:15	BWLvsHCP	2.6 – 3.7	33%	15%	67%	2:3 5%	1.00 – 2.00	Medium
09 Oct 2026, 19:30	PIVvsKAC	1.8 – 5.0	11%	7%	89%	1:5 5%	0.32 – 2.68	Medium
09 Oct 2026, 19:45	MHCvsHCI	3.9 – 2.2	75%	13%	25%	3:2 5%	2.26 – 0.74	Medium
11 Oct 2026, 16:00	VICvsHCP	2.3 – 3.4	33%	16%	67%	2:3 6%	0.99 – 2.01	Medium
11 Oct 2026, 16:00	AVSvsG99	1.8 – 2.9	30%	17%	70%	1:2 7%	0.91 – 2.09	Medium
11 Oct 2026, 16:00	OLLvsFTC	4.6 – 3.2	69%	13%	31%	4:3 4%	2.07 – 0.93	Medium
11 Oct 2026, 17:00	PIVvsBWL	2.4 – 4.0	27%	14%	73%	2:3 5%	0.82 – 2.18	Medium
11 Oct 2026, 17:00	MHCvsHCB	2.5 – 3.5	35%	15%	65%	2:3 5%	1.04 – 1.96	Medium
11 Oct 2026, 17:30	KACvsRBS	4.1 – 2.4	75%	13%	25%	4:2 5%	2.25 – 0.75	Medium
11 Oct 2026, 17:30	HCIvsVSV	2.4 – 3.4	33%	15%	67%	2:3 6%	0.98 – 2.02	Medium
16 Oct 2026, 18:30	FTCvsPIV	3.7 – 2.7	65%	15%	35%	3:2 5%	1.95 – 1.05	Medium
16 Oct 2026, 19:15	VSVvsG99	2.4 – 2.9	41%	17%	59%	2:2 6%	1.22 – 1.78	Medium
16 Oct 2026, 19:15	KACvsVIC	3.9 – 2.2	75%	13%	25%	3:2 5%	2.24 – 0.76	Medium
16 Oct 2026, 19:15	RBSvsBWL	3.2 – 3.3	49%	16%	51%	3:3 5%	1.46 – 1.54	Medium
16 Oct 2026, 19:15	OLLvsHCB	3.1 – 4.0	36%	14%	64%	3:3 4%	1.09 – 1.91	Medium
16 Oct 2026, 19:45	HCPvsHCI	4.2 – 1.9	82%	11%	18%	4:1 5%	2.45 – 0.55	Medium
17 Oct 2026, 17:00	MHCvsAVS	3.3 – 2.0	71%	15%	29%	3:2 6%	2.12 – 0.88	Medium
17 Oct 2026, 19:15	VICvsPIV	3.5 – 2.2	70%	15%	30%	3:2 6%	2.09 – 0.91	Medium
18 Oct 2026, 13:30	VSVvsHCP	2.4 – 3.6	31%	15%	69%	2:3 6%	0.94 – 2.06	Medium
18 Oct 2026, 15:30	FTCvsKAC	2.3 – 4.7	18%	10%	82%	2:4 5%	0.55 – 2.45	Medium
18 Oct 2026, 17:00	MHCvsAVS	3.3 – 2.0	71%	15%	29%	3:2 6%	2.12 – 0.88	Medium
18 Oct 2026, 17:30	HCIvsOLL	2.5 – 4.7	20%	11%	80%	2:4 5%	0.61 – 2.39	Medium
18 Oct 2026, 18:00	HCBvsBWL	4.0 – 2.5	72%	14%	28%	3:2 5%	2.15 – 0.85	Medium
21 Oct 2026, 18:30	G99vsMHC	2.7 – 2.7	50%	18%	50%	2:2 6%	1.49 – 1.51	Medium
21 Oct 2026, 19:15	VSVvsFTC	3.3 – 3.0	55%	16%	45%	3:3 5%	1.65 – 1.35	Medium
21 Oct 2026, 19:15	KACvsAVS	4.1 – 1.7	84%	11%	16%	4:1 6%	2.52 – 0.48	Medium
21 Oct 2026, 19:15	RBSvsOLL	3.2 – 4.0	38%	14%	62%	3:4 4%	1.14 – 1.86	Medium
21 Oct 2026, 19:15	BWLvsVIC	3.1 – 3.0	51%	17%	49%	3:2 5%	1.54 – 1.46	Medium
21 Oct 2026, 19:45	HCPvsPIV	4.4 – 1.9	83%	10%	17%	4:1 5%	2.50 – 0.50	Medium
21 Oct 2026, 19:45	HCBvsHCI	4.5 – 1.8	86%	9%	14%	4:1 6%	2.57 – 0.43	Medium

Predicted vs actual 26

17 hit · 9 missed · 1 exact

Winner called right65.4%17 of 26 games

Exact scoreline13.8% of games

Mean goal error±1.92on the total goals in a game

Mean prediction44.9%home actually won 53.8%

Every finished game with the prediction it had beforehand, sortable by any column

03 Oct 2026, 17:30	G99vsHCI	68%	3.4 – 2.2	3:2	3:0	Hit	-2.6
02 Oct 2026, 19:45	HCBvsKAC	33%	2.6 – 3.8	2:3	4:0	Miss	-2.4
02 Oct 2026, 19:45	HCPvsOLL	48%	3.4 – 3.5	3:3	4:2	Miss	-0.9
02 Oct 2026, 19:30	PIVvsRBS	32%	2.5 – 3.6	2:3	1:4	Hit	-1.1
02 Oct 2026, 19:15	VICvsHCI	52%	3.0 – 2.9	3:2	4:0	Hit	-1.9
02 Oct 2026, 19:15	BWLvsG99	40%	2.6 – 3.2	2:3	3:2 SO	Miss	-0.8
02 Oct 2026, 19:15	AVSvsFTC	46%	2.5 – 2.7	2:2	2:5	Hit	+1.9
02 Oct 2026, 19:15	VSVvsMHC	23%	1.9 – 3.7	1:3	7:2	Miss	+3.4
30 Sept 2026, 19:15	AVSvsVSV	39%	2.3 – 2.9	2:2	1:0	Miss	-4.2
30 Sept 2026, 19:15	HCIvsPIV	49%	2.7 – 2.8	2:2	5:4	Miss	+3.5
30 Sept 2026, 19:15	RBSvsHCB	29%	2.5 – 4.0	2:3	1:4	Hit	-1.5
30 Sept 2026, 19:15	KACvsMHC	69%	3.6 – 2.3	3:2	2:4	Miss	+0.1
30 Sept 2026, 18:30	G99vsHCP	46%	2.6 – 2.8	2:2	1:4	Hit	-0.4
30 Sept 2026, 18:30	FTCvsBWL	39%	2.8 – 3.5	2:3	3:5	Hit	+1.7
27 Sept 2026, 18:00	OLLvsVSV	59%	3.6 – 3.1	3:3	6:2	Hit	+1.3
27 Sept 2026, 17:30	HCIvsHCB	17%	2.0 – 4.4	2:4	3:4	Hit	+0.5
27 Sept 2026, 17:30	KACvsG99	61%	3.0 – 2.3	2:2	6:2	Hit	+2.7
27 Sept 2026, 17:00	HCPvsBWL	56%	3.1 – 2.7	3:2	5:4	Hit	+3.2
27 Sept 2026, 17:00	PIVvsAVS	40%	2.3 – 2.9	2:2	2:3	Hit	-0.1
27 Sept 2026, 15:30	FTCvsRBS	42%	2.7 – 3.2	2:3	3:6	Hit	+3.0
26 Sept 2026, 19:15	VICvsMHC	37%	2.3 – 3.0	2:3	2:6	Hit	+2.7
26 Sept 2026, 14:30	HCIvsAVS	39%	2.7 – 3.3	2:3	2:0	Miss	-4.0
25 Sept 2026, 19:15	BWLvsPIV	58%	3.0 – 2.5	2:2	5:3	Hit	+2.5
25 Sept 2026, 19:15	OLLvsKAC	27%	2.6 – 4.1	2:4	3:7	Hit	+3.3
25 Sept 2026, 19:15	RBSvsVSV	52%	3.1 – 2.9	3:2	2:4	Miss	-0.0
25 Sept 2026, 18:30	G99vsVIC	63%	3.0 – 2.2	3:2	3:2 OT	Hit★	-0.2

Each row's prediction was built from the games played before that date only — nothing after it. Sort by Goals Δ or Call to find where the model went wrong. A ★ marks a game whose exact scoreline it also got right.

Biggest upsets

VSVvsMHC
02 Oct 2026, 19:15 · predicted 23% home
7:2home won
KACvsMHC
30 Sept 2026, 19:15 · predicted 69% home
2:4away won
HCBvsKAC
02 Oct 2026, 19:45 · predicted 33% home
4:0home won
AVSvsVSV
30 Sept 2026, 19:15 · predicted 39% home
1:0home won
HCIvsAVS
26 Sept 2026, 14:30 · predicted 39% home
2:0home won

Games where the model was most confident in the side that lost.

Best calls

HCIvsHCB
27 Sept 2026, 17:30 · predicted 17% home
3:4as predicted
OLLvsKAC
25 Sept 2026, 19:15 · predicted 27% home
3:7as predicted
RBSvsHCB
30 Sept 2026, 19:15 · predicted 29% home
1:4as predicted
G99vsHCI
03 Oct 2026, 17:30 · predicted 68% home
3:0as predicted
PIVvsRBS
02 Oct 2026, 19:30 · predicted 32% home
1:4as predicted

Confident calls that came in. Note these are cheap to collect — a good model should have many of them, which is why the upsets matter more.

Model vs the betting market

20 games with closing odds

Model and market scored on the same games
Measure	Model	Market	Difference
Winner called right	65.0%	70.0%	−5.0 pp
Brier score	0.209	0.194	−0.015
Log loss	0.605	0.576	−0.029

Market edge0.015Brier points, on the same games

Flat-stake return12.0%one unit on the model's pick, every game

Bookmaker margin9.9%mean overround in the prices

They disagree35.0%model right 42.9% of those

Both are scored on the same 20 games, using the same overtime coin flip, so the comparison is like-for-like. Odds are de-vigged by normalising the three reciprocals to 1. The market is better calibrated, which is the expected result — closing odds absorb injuries, line-ups and money this model never sees.

The flat-stake figure is a sanity check, not a strategy: the three-way price is on regulation time, so any game decided in overtime or a shootout loses the stake. Treat a positive number on a few hundred games as luck until it survives several seasons.

How well does this model do?

26 games scored

Only 26 games have been scored so far this season — far too few to judge a model on. Switch to a finished season for a stable read.

Winner called right65.4%home-only baseline 53.8%

Brier score0.228baseline 0.257 · lower is better

Log loss0.649baseline 0.708

Beats the baseline by0.029Brier points of skill

Total-goals error±1.92mean absolute error per game

Exact scoreline3.8%hit rate on the likeliest score

Accuracy as the season went onCumulative share of games called right, against picking the home side every time

Home-only baseline
Model

Table view

Games scored	Home-only baseline	Model
2	50.0%	50.0%
4	50.0%	75.0%
6	50.0%	66.7%
8	37.5%	75.0%
10	50.0%	80.0%
12	50.0%	83.3%
14	42.9%	85.7%
16	37.5%	81.3%
18	44.4%	72.2%
20	45.0%	70.0%
22	50.0%	68.2%
24	50.0%	66.7%
26	53.8%	65.4%

Both lines are cumulative, so they settle as the sample grows. The gap between them is the model's edge over the naive pick.

CalibrationWhen the model says 70%, does the home side win 70% of the time?

Predicted
Actual

Table view

Predicted home win %	Predicted	Actual	Games
30–40	37%	57%	7
40–50	45%	33%	6
50–60	56%	80%	5

The two lines tracking each other means the probabilities can be taken at face value. Where "actual" sits above "predicted", the model is under-confident in the home side; below, it is over-confident. Check the game count before reading much into a bucket — the extremes hold far fewer games than the middle. Buckets with fewer than five games are left out.

Every game is predicted using only the games played before it — a walk-forward test, so these are the numbers the model would really have produced. The baseline is "always back the home side", priced at the home-win rate known at the time. Team strengths start from the 2025/26 final table.

How the prediction works

Each team gets an attack and a defence multiplier from the goals it has scored and conceded, measured against the league average.
A fixture's expected goals are the league's home (or away) scoring rate times the attacking team's attack times the defending team's defence — so home advantage comes out of fitting home and away separately, not from a bonus added on top.
Both sides' goals are then treated as independent Poisson draws, which gives a probability for every scoreline rather than one guess, and from that the win, overtime and exact-score numbers.
Rates are shrunk toward the prior with six games of weight, so an early 7-1 win does not make a team look unbeatable. This is why fixtures are marked low confidence until both teams have played a few games.
Overtime and shootouts are treated as a coin flip. The feeds carry nothing that reliably predicts a shootout winner, and pretending otherwise would just be noise.

Goal models like this one capture scoring rates, not injuries, suspensions, travel, goalie rotation or roster changes. Treat the probabilities as a calibrated baseline, not a forecast — and read the accuracy panel above before trusting any single number.