2024-2025 NHL Elo Ratings & Projections

Last updated Thu, Apr 3, 2025, 9:42 PM EDT.

Welcome to our 2024-25 NHL elo projections!

This website is inspired by 538's NHL Elo ratings, which are sadly no longer available on their site. We provide season-long and game-level projections, updated daily. See below the table for how it works.

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							Based on 50,000 simulations

1	Dallas StarsDAL 49-21-4	West	1610	+47	+21	113	✓	69%	41%	28%	18.6%
2	Winnipeg JetsWPG 51-20-4	West	1595	+64	-5	115	✓	69%	34%	22%	13.5%
3	Carolina HurricanesCAR 46-24-4	East	1576	+18	+10	106	✓	69%	40%	22%	10.3%
4	Tampa Bay LightningTBL 44-26-5	East	1576	+41	+14	102	>99.9%	62%	37%	20%	9.3%
5	Washington CapitalsWSH 48-18-9	East	1565	+74	-21	114	✓	74%	39%	20%	8.8%
6	Vegas Golden KnightsVEG 45-21-8	West	1570	+36	+14	108	✓	68%	38%	15%	8.0%
7	Colorado AvalancheCOL 46-26-4	West	1582	+24	+1	104	>99.9%	42%	20%	12%	6.7%
8	Toronto Maple LeafsTOR 46-25-4	East	1555	+4	+11	104	✓	61%	28%	14%	5.5%
9	Edmonton OilersEDM 43-26-5	West	1563	-18	-5	101	99.9%	54%	28%	10%	5.2%
10	Florida PanthersFLA 44-27-4	East	1558	-7	-11	101	>99.9%	49%	25%	13%	4.9%
11	Los Angeles KingsLAK 42-23-9	West	1549	+27	+3	103	>99.9%	47%	22%	8%	3.6%
12	St. Louis BluesSTL 41-28-7	West	1539	+51	+19	96	93.0%	28%	11%	4%	1.8%
13	New Jersey DevilsNJD 40-29-7	East	1529	-1	+5	95	99.6%	32%	14%	5%	1.7%
14	Ottawa SenatorsOTT 40-29-6	East	1511	+13	-2	95	99.2%	28%	9%	3%	1.0%
15	Minnesota WildMIN 41-28-7	West	1498	-17	-13	96	93.1%	19%	6%	1%	0.5%
16	New York RangersNYR 36-32-7	East	1514	-40	+5	86	28.2%	9%	3%	1%	0.4%
17	Montreal CanadiensMTL 35-30-9	East	1478	+39	-3	88	49.4%	11%	3%	<1%	0.2%
18	Columbus Blue JacketsCBJ 34-30-9	East	1470	+59	+5	86	18.8%	4%	1%	<1%	0.1%
19	Calgary FlamesCGY 35-27-12	West	1474	+3	-3	91	11.8%	2%	<1%	<1%	0.0%
20	New York IslandersNYI 32-32-10	East	1472	-33	-22	82	2.4%	<1%	<1%	<1%	0.0%
21	Utah Hockey ClubUTA 34-29-12	West	1485	+2	-6	87	0.7%	<1%	<1%	<1%	0.0%
22	Detroit Red WingsDET 34-33-7	East	1482	-15	+1	82	2.3%	<1%	<1%	<1%	0.0%
23	Vancouver CanucksVAN 34-28-13	West	1489	-51	-5	89	1.5%	<1%	<1%	<1%	0.0%
24	Buffalo SabresBUF 32-36-6	East	1480	-19	+21	78	0.0%	<1%	<1%	—	—
25	Pittsburgh PenguinsPIT 30-34-11	East	1453	-51	-3	78	0.0%	—	—	—	—
26	Anaheim DucksANA 33-33-8	West	1452	+40	+5	81	0.0%	—	—	—	—
27	Seattle KrakenSEA 32-38-6	West	1473	-11	+8	76	—	—	—	—	—
28	Boston BruinsBOS 30-36-9	East	1455	-88	-23	76	—	—	—	—	—
29	Nashville PredatorsNSH 27-40-8	West	1449	-85	-8	69	—	—	—	—	—
30	Philadelphia FlyersPHI 31-36-9	East	1439	-34	+4	77	—	—	—	—	—
31	Chicago BlackhawksCHI 21-44-10	West	1376	-44	-7	57	—	—	—	—	—
32	San Jose SharksSJS 20-44-10	West	1353	-28	+1	56	—	—	—	—	—

As of:

What is this?

This is an elo forecast based on FiveThirtyEight's deprecated NHL Elo model and forecast. You can read about how the original version works on FiveThirtyEight's website. Model credit to @ryanabest and @neil_paine, with modifications outlined below. This website is not affiliated with FiveThirtyEight or ABC News.

Current Version

The current version of the projections builds on FiveThirtyEight's model, biasing parameters to the salary-cap era. We tune home-ice advantage from 50 to 42, and increase the K-factor from 6 to 8. We also add a Vegas totals bias to the teams' season starting elo, rather than relying solely on a regressed season-ending elo, which short-cuts learning any off-season movement.

How does it work?

Each team is given an elo score, which is effectively their strength relative to other teams. At the start of the eason, this score is a blend of their previous season's ending elo, regressed to the mean, and an implied elo derived from the Vegas season point total projections. Before each game, we compute the probability of each team winning based on their elo ratings and other factors like home ice advantage. After each game is played, we update the teams' scores based based on who won, by how much, who was originally expected to win, etc.

To simulate the season, we go game by game, randomly picking a winner of each game weighted on their pre-game probabilities. We then update their elo rating as described above as if they really played the game, and continue to the next game. We do this for the whole season (including playoffs) tens of thousands of times, recording how each team did in each full season simulation.

We then average their results across simulated seasons to get the probability of making the playoffs or winning the cup, etc., which are presented in the table above. For example, if the Edmonton Oilers won the cup 6,000 times in 50,000 simulations, they would show a 12% chance of winning the cup in the table above. If the Leafs won the cup 5,000 times, the table would show 10%, but we'd all know the true probability is still 0.

Is it accurate?

These projections have been shown to be exceptionally well-calibrated over the years when 538 was doing it, and last year when I did it. So across a season, they're quite reliable. When you factor in the simplicity of the modeling, the ROI on effort to results is quite frankly astounding. (As an aside, other projections websites go to mind-bending lengths incuding things like puck locations and player tracking, and achieve only marginally better results.)

However, there are shortfalls to the system, especially at the game level. It doesn't account for day-to-day changes like injuries, trades, suspensions, coaching changes, etc. When these things happen, it can take a few games for the elo score to capture the impact, and so any given game could be poorly calibrated in the short term. One way to think about this is if the Vegas money line is significantly different from the elo projection, it's not a value find but an indication that there's a significant factor the model doesn't know about.

How do I use this to gamble?

You don't. I'm just some random guy on the internet (see below) and this could go away at any time. It's made for entertainment and hockey obsession purposes only.

Why do this?

I'm a super fan of both hockey and elo ratings. So when I learned that FiveThirtyEight wasn't doing their forecasts last season, I shed a few tears. Ironically, a hockey injury gave me some extra time back in my week, which I used to build this.

Who made this?

I'm a software engineer, machine learning practioner and beer league hockey player. My day job is building software for Coastline, a driving education startup.