Tag: Hockey Analytics

What Is Corsi in Hockey?

IHM Knowledge Center

What Is Corsi in Hockey?

How do analysts use shot attempts to estimate puck possession and overall game control?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

Corsi is a statistic that counts all shot attempts, including shots on goal, missed shots, and blocked shots. It is used as a proxy for puck possession and offensive pressure.

Full Explanation

Corsi is one of the foundational metrics in hockey analytics. It tracks every attempt to direct the puck toward the net.

This includes:

  • Shots on goal
  • Missed shots
  • Blocked shots

The idea behind Corsi is simple. Teams that control the puck more tend to generate more shot attempts over time.

Because direct possession time is difficult to track accurately, Corsi is used as a practical way to estimate which team is controlling play.

Corsi is often expressed as a percentage. If a team has 55 percent Corsi, it means they are taking more shot attempts than their opponent.

How Corsi Reflects Game Control

Corsi helps show which team is spending more time in the offensive zone and applying pressure.

Teams with strong Corsi numbers typically:

  • Control puck possession
  • Maintain offensive zone time
  • Force opponents to defend

However, Corsi does not measure shot quality. A team can have strong Corsi but still create low-danger chances.

This is why Corsi should be combined with other metrics like expected goals and high-danger chances.

NHL vs IIHF Context

Corsi is most commonly used in NHL analytics due to detailed tracking data.

In IIHF hockey, the same concept applies, but interpretation may vary depending on style of play and data availability.

The core principle remains the same across all levels.

Why Corsi Is Controversial

Corsi is controversial because it does not differentiate between dangerous and non-dangerous shots.

Fans may see a high Corsi percentage and assume dominance, but coaches understand that not all shot attempts create real scoring threats.

A team may generate many low-quality shots while the opponent focuses on fewer but better chances.

This difference creates debate about how much value Corsi should have in evaluation.

Edge Case: High Corsi but Weak Offense

A key edge case occurs when a team has strong Corsi numbers but struggles to score.

This usually happens when:

  • Most shots come from the perimeter
  • The slot is well defended
  • The goalie has clear visibility
  • There is little pre-shot movement

In this case, Corsi reflects pressure but not effective offense.

This is why combining Corsi with shot quality metrics is critical.

IHM Signal System: How to Read Corsi

To interpret Corsi correctly, focus on these signals:

  • Shot location: Are attempts coming from dangerous areas?
  • Game state: Is the team leading or trailing?
  • Shot type: Quick chances or low-danger volume?
  • Defensive structure: Is the opponent allowing outside shots?
  • Trend: Is Corsi consistent over time?

Trigger-level rule:

If a team has high Corsi but low high-danger chances, the offensive pressure is almost always inefficient.

This is one of the most important signals when using Corsi.

IHM Insight: Why Corsi Is Misunderstood

Corsi is often misunderstood because it is treated as a direct measure of dominance.

In reality, it measures volume, not quality.

Two teams can have similar Corsi numbers but very different scoring potential depending on how those shots are created.

Understanding this difference is essential for proper analysis.

Mini Q&A

What does Corsi measure?
Total shot attempts.

Is Corsi the same as possession?
No, but it is used as a proxy.

What is a good Corsi percentage?
Above 50 percent.

Does Corsi measure scoring chances?
No, only shot attempts.

Should Corsi be used alone?
No, it should be combined with other metrics.

Why This Rule Exists

Corsi exists to provide a simple way to measure puck possession and offensive pressure through shot attempts.

It allows analysts to compare teams and players even when direct possession data is not available.

Key Takeaways

  • Corsi counts all shot attempts
  • It is used as a proxy for possession
  • High Corsi means more offensive pressure
  • It does not measure shot quality
  • Context is required for proper interpretation

What Is Expected Goals (xG) in Hockey?

IHM Knowledge Center

What Is Expected Goals (xG) in Hockey?

How do analysts estimate how likely a shot is to become a goal, and why is xG one of the most important modern hockey metrics?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

Expected goals (xG) is a metric that estimates the probability of a shot becoming a goal based on factors like location, angle, type of play, and pre-shot movement.

Full Explanation

Expected goals, or xG, is one of the most advanced and widely used metrics in hockey analytics.

It assigns a probability value to each shot based on how likely that shot is to result in a goal.

For example:

  • A shot from the slot may have a high xG value
  • A shot from the boards may have a low xG value

These probabilities are based on historical data, analyzing thousands of similar shots to determine scoring likelihood.

By adding all shot probabilities together, analysts can estimate how many goals a team should have scored based on chance quality.

How xG Reflects Offensive Performance

xG is used to evaluate how dangerous a team’s offense actually is.

A team with high xG is creating strong scoring chances, even if the actual goals have not been scored yet.

A team with low xG may be taking many shots, but those shots are likely low quality.

This is why xG is often considered more accurate than simple shot totals.

NHL vs IIHF Context

xG models are most advanced in the NHL due to detailed tracking data.

In IIHF competitions, xG can still be applied, but models may be less detailed depending on available data.

The core idea remains the same across all levels of hockey.

Why xG Is Controversial

xG can be controversial because it relies on models and probabilities rather than actual outcomes.

Fans may question why a team with higher xG lost the game.

Coaches understand that xG reflects chance quality, not guaranteed results.

Finishing ability, goaltending performance, and game situations can all cause differences between expected and actual goals.

This creates debate about how much weight xG should have in evaluation.

Edge Case: High xG but No Goals

A common edge case occurs when a team generates high xG but fails to score.

This can happen when:

  • The opposing goalie performs at a high level
  • Shots miss key opportunities
  • Execution in finishing is weak
  • Rebounds are not converted

In this situation, xG suggests strong offensive play, but the result does not reflect it.

This is why xG should be used to evaluate performance, not just outcomes.

IHM Signal System: How to Read xG

To interpret xG correctly, focus on these signals:

  • Chance type: Slot shots, rebounds, rush chances
  • Shot sequence: Was there pre-shot movement?
  • Traffic: Was the goalie screened?
  • Consistency: Are high xG chances repeated?
  • Game state: When were chances created?

Trigger-level rule:

If a team consistently generates high xG through slot chances and lateral puck movement, goals will almost always follow over time.

This is one of the most reliable indicators of offensive strength.

IHM Insight: Why xG Is Misunderstood

xG is misunderstood because people expect it to match actual goals in every game.

In reality, it measures probability, not certainty.

A team can win with low xG or lose with high xG in a single game, but over time, results tend to align more closely with expected values.

This is why xG is more useful over multiple games rather than single outcomes.

Mini Q&A

What does xG mean?
Expected goals.

What does xG measure?
Shot quality and scoring probability.

Is higher xG better?
Yes, it usually means better chances.

Does xG guarantee goals?
No, it only estimates probability.

Should xG be used alone?
No, it should be combined with other analysis.

Why This Rule Exists

xG exists to measure scoring chance quality instead of relying only on shot totals.

It provides a more accurate way to evaluate offensive performance and predict future results.

Key Takeaways

  • xG measures scoring probability
  • It is based on shot quality
  • Higher xG means better chances
  • It does not guarantee outcomes
  • Best used over larger sample sizes
IHM Academy · Performance Metrics Masterclass - Lesson 5

IHM Academy · Performance Metrics Masterclass – Lesson 6

Performance Metrics Masterclass – Lesson 6: Possession Chains & Puck Retrieval Metrics

Modern hockey is not just about who shoots more, but about who owns the puck longer in dangerous sequences. Possession is built in chains: recoveries, passes, attacks, rebounds, and pressure resets. Every time your team wins a puck and turns it into a sustained sequence, you are building a possession chain.

Puck retrieval metrics show who actually wins the right to attack again – after dump-ins, rebounds, blocked shots, broken plays, and loose pucks on the wall. Elite programs track these numbers shift by shift, player by player.

You don’t just want shots. You want repeated, connected possessions that suffocate the opponent.

🎯 Primary Objectives

  • Measure how often your team turns loose pucks into new possessions.
  • Identify players who extend offensive pressure through retrievals.
  • Understand which lines create multi-chance sequences, not one-and-done attacks.
  • Link retrieval metrics to xG chains, zone time, and fatigue on the opponent.

🧠 Key Concepts

1. Possession Chains

A possession chain is the full sequence from winning the puck to losing it again:

  • Recovery → pass → entry or cycle → shot → rebound / retrieval → second attack.

Instead of looking at a single shot, we look at the entire sequence and ask:

  • How many events (passes, shots, retrievals) did we create in this chain?
  • How much xG was generated across the whole sequence?
  • How long did we keep the puck before turning it over?

Teams with strong possession chains don’t just “take shots” – they live in the offensive zone.

2. Puck Retrieval Metrics

Retrievals are the glue that connect one action to the next. Key metrics:

  • Offensive Zone Retrieval % – percentage of dump-ins, rebounds and loose pucks your team recovers in the O-zone.
  • Defensive Zone Retrieval % – how often you win races to loose pucks in your own end and start a new exit.
  • Rebound Retrieval % – share of rebounds your forwards win after your first shot.
  • Wall Battle Win Rate – how often your players come out with the puck after contact on the boards.

These numbers show who keeps plays alive when the puck is up for grabs.

3. Chain Length & Quality

Not all chains are equal. We care about:

  • Average chain length (in events or seconds of puck possession).
  • xG per chain – how much expected offense each chain produces.
  • Multi-shot chain rate – percentage of chains that produce 2+ shots.

Longer, higher-quality chains wear down defenders, draw penalties, and create momentum swings.

🧩 Role Impact

Defensemen

  • Clean first touches after retrievals: off the glass is a last resort, not a habit.
  • Smart keep-ins at the blue line extend chains and pin the opponent.
  • Good gap and stick position in the neutral zone create easy retrievals for teammates.

Centers

  • Primary support on loose pucks in all three zones.
  • Turn retrievals into immediate middle-lane plays instead of safe dumps.
  • Drive the “second wave” after initial shots – arrive in time to win rebounds.

Wingers

  • First on the forecheck; first on the wall on dump-ins.
  • Win races and seal the inside lane during battles.
  • Turn 50/50 pucks into offensive starts, not defensive scrambles.

🔧 Core Metrics & What They Mean

  • O-Zone Retrieval % – ability to keep the attack alive after dump-ins and shots.
  • Rebound Retrieval % – pressure on the goalie and defense after the first shot.
  • Chain xG – how dangerous your average possession sequence is.
  • Multi-shot Chain Rate – indicator of sustained pressure, not one-and-done hockey.
  • Wall Battle Win Rate – physical and technical execution under pressure.

💬 Coach Mark Lehtonen says

“Great teams don’t play one-shot hockey.
They build waves of pressure from every loose puck.”

“If you can’t retrieve, you can’t attack twice.
The second chance is where playoff games are won.”

❓ Q&A – Possession & Retrieval

Q1: Why are puck retrieval metrics more informative than just shot counts?

A: Shot counts only show how many attempts you had, not how you got them. Retrieval metrics reveal whether your team can extend attacks, win second chances and live in the offensive zone. A team with fewer shots but elite retrieval and chain xG can be more dangerous than a volume team that plays one-and-done hockey.

Q2: Which players usually lead in retrieval metrics?

A: Often it’s not the top scorers but the “engine” players – strong-skating wingers, smart centers and mobile defensemen who read loose pucks early. They may not finish every play, but they give your scorers extra chances by extending chains.

Q3: How can a coach improve O-zone retrieval %?

A: Focus on routes and timing on the forecheck, not just effort. F1 drives the puck, F2 reads the wall, F3 protects the middle. Teach players to seal the inside, keep sticks in lanes and react as a unit when the puck is chipped or blocked. The earlier the read, the easier the win.

Q4: How do these metrics help with scouting and player evaluation?

A: Retrieval and possession-chain data identify players who drive winning hockey even without big point totals. A winger who consistently wins pucks back and extends sequences can be more valuable than a scorer who disappears when the puck is contested.

❌ Common Mistakes

MistakeConsequence
Watching the shot instead of reading the reboundOpponents win easy clears and kill momentum
Flying past the play on the forecheckNo inside position; 50/50 pucks become 30/70
Defensemen defaulting to rims under light pressureLost possession chains and uncontrolled exits
Forwards circling high instead of stopping on pucksLost battles on the wall; no second chances
No tracking of retrieval metricsCoaches misjudge effort vs. actual possession impact

🧪 Micro-Drills

  • Rebound Hunt Drill – shot from the point, two forwards vs. two defenders battle for every rebound; track retrieval %, not just goals.
  • Dump-In Retrieval Race – structured dump with F1/F2/F3 routes; scoring only counts if the puck is retrieved and a second shot is created.
  • Wall Battle into Cycle – 1v1 or 2v2 on the boards; winner must make a play off the wall to extend the chain, not just clear.

🧱 Summary

Possession chains and puck retrieval metrics explain why some teams feel relentless. They win loose pucks, extend sequences and attack in waves. When you track and train these details, you move from counting shots to controlling the game.

You don’t just want the first chance. You want the next one, and the one after that.


Performance Metrics Master Lessons | IHM Academy

Performance Metrics Master Lessons | IHM Academy

A pro-level module breaking down modern NHL analytics: shot-quality models, high-danger scoring, Ice Tilt momentum, speed tracking, projected goals, possession metrics and elite player evaluation. Lessons crafted in the signature coaching style of Mark Lehtonen for the IHM Academy.

IHM Academy · Performance Metrics - How Coach Mark Lehtonen Turns Performance Metrics Into Structured Match Verdicts

IHM Academy · Performance Metrics - How Coach Mark Lehtonen Turns Performance Metrics Into Structured Match Verdicts



IHM Academy · Performance Metrics Masterclass - Lesson 5

IHM Academy · Performance Metrics Masterclass - Lesson 5


Performance Metrics Masterclass - Lesson 4: Zone Entries, Exits & Transition Speed

IHM Academy · Performance Metrics Masterclass - Lesson 4


IHM Academy · Performance Metrics Masterclass - Lesson 3

Performance Metrics Masterclass - Lesson 3 : Zone Entry Efficiency & Controlled Breakout Success


IHM Academy · Performance Metrics Masterclass - Lesson 2

IHM Academy · Performance Metrics Masterclass - Lesson 2


IHM Academy - Performance Metrics Masterclass • Lesson 1

IHM Academy - Performance Metrics Masterclass • Lesson 1


IHM Performance Metrics Report: Why the Ducks and Utah Mammoth Suddenly Look Like Analytics Superpowers

IHM Performance Metrics Report: Why the Ducks and Utah Mammoth Suddenly Look Like Analytics Superpowers

Date: November 8, 2025 | Author: IHM News Analytics


Why the Ducks and Utah Mammoth suddenly look like analytics superpowers

A deep breakdown of two surprising engines of the 2025-26 NHL season

The first month of the season has delivered two unexpected machines of chaos: Anaheim Ducks, suddenly the brightest offensive show in the West, and Utah Mammoth, who instantly found an elite play-driver in Nick Schmaltz.

But behind the flurries of goals, comebacks and nightly highlights lies a far more revealing truth. This is an analytics-based evolution built on:

  • high-danger efficiency
  • elite transitional play
  • explosive speed clusters
  • possession metrics that indicate sustainability

IHM EDGE broke down both teams under the microscope – here’s what we found.


🦆 SECTION I – Anaheim Ducks: Inside the engine of a sudden powerhouse

1. High-danger ecosystem

Anaheim aren’t just scoring a lot – they are scoring the right way. The Ducks have already generated 28 high-danger goals, more than most of their division combined. Chris Kreider and Cutter Gauthier are currently among the top high-danger producers in the NHL.

Carlsson, Sennecke and Terry form a constant pressure triangle built on:

  • fast zone entries
  • short-link passing
  • finishes from the kill zone (2-4 meters)

This is not randomness - it’s a system. And it works.

2. Cutter Gauthier: The EDGE monster exceeding every projection

Gauthier is one of the most “unstoppable” analytical profiles in the league right now. His EDGE metrics look engineered:

  • average shot speed – 97th percentile
  • speed bursts – 97th percentile
  • hardest shot – 93rd percentile
  • mid-range goals – leads NHL
  • Goals Above Projected – +5.91 (1st in NHL)

He scores shots that models classify as low-probability. When a player beats the model itself – we’re dealing with elite talent.

3. Territorial control – Ice Tilt as a predictor of future success

Anaheim currently rank No. 1 in the NHL in first-period Ice Tilt advantage. This means they take control of rink territory and game tempo early.

Carlsson (+63) and Gauthier (+60) dominate 5v5 shot differential like established superstars – at age 20 and 21.

4. Goaltending stability

Dostal has quietly become a stabilizer:

  • elite mid-range SV%
  • 7-3-1 record
  • 5v5 save% above league average

For a team that has lacked a foundation in net for years, this is transformative.


🦬 SECTION II – Utah Mammoth: Schmaltz’s reinvention and the rise of a new top-six

Utah play fast, aggressive and structured – but their entire offensive shape is glued together by one player: Nick Schmaltz, the most underrated starter of the season.

1. Shot profile: dangerous from every lane

Schmaltz is one of the rare forwards producing elite volume from all three shot tiers:

  • high-danger – 96th percentile
  • mid-range – 95th percentile
  • long-range – 92nd percentile

42 shots in 12 games – the best pace of his entire career. Utah are top-two in shot differential, which confirms structure, not luck.

2. High-danger finishing touch

Five high-danger goals – fourth in the NHL. Two goals on deflections – placing him in rare company with Crosby and Miles Wood.

Schmaltz has long been a high-danger creator, but now he’s finishing at a career-high level.

3. Speed metrics: Utah = a missile

Schmaltz:

  • 20+ mph bursts – 84th percentile
  • total distance – 93rd percentile

Utah as a whole:

  • Cooley – second-fastest skater in the NHL
  • team – 4th in total speed bursts
  • shots allowed per game – 2nd fewest in NHL

This is a team that skates fast without losing structural discipline.

4. Chemistry: Keller – Schmaltz – Hayton

This long-developing trio finally has the personnel to play at full throttle. They drive Utah’s PP1 and tempo game, making possession swings almost automatic.


🚀 SECTION III – What Ducks and Mammoth have in common

Both teams:

  • dominate high-danger creation
  • apply speed as a core identity, not just a tool
  • are led by young stars who already think like veterans
  • show sustainable possession trends
  • benefit from EDGE-positive profiles across the top six
  • look structurally built, not statistically lucky

🎯 IHM VERDICT

Ducks:

Legitimate contenders for a top-2 finish in the Pacific Division. Their metrics match conference finalists – not pretenders.

Utah Mammoth:

Massively underrated playoff candidates. Their top-six is good enough to drag them into contention all season.


Questions & Answers | IHM Performance Metrics

Why are the Anaheim Ducks performing so well this season?

The Ducks rank among the NHL’s best teams in high-danger scoring, first-period territorial control (Ice Tilt) and 5-on-5 possession metrics. Their young core, led by Carlsson and Gauthier, drives elite shot volume and transition pace.

What makes Cutter Gauthier’s analytics profile elite?

Gauthier ranks in the 93rd-99th percentiles in shot power, speed bursts, midrange scoring and goals above expected. He consistently beats projected goal models.

Why is Nick Schmaltz breaking out for the Utah Mammoth?

Schmaltz produces high-volume shots from every scoring tier and ranks top-five in high-danger goals this season. His skating metrics and chemistry with Keller elevate Utah’s entire top six.

Are the Ducks and Mammoth legitimate playoff contenders?

Both teams show sustainable shot-differential and chance-generation metrics, suggesting long-term competitiveness rather than early-season variance.