Tag: Hockey Stats

What Is Shooting Percentage in Hockey Analytics? | IHM

IHM Knowledge Center

What Is Shooting Percentage in Hockey Analytics?

Why do some players score more goals with fewer shots, and how does shooting percentage explain finishing efficiency?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

Shooting percentage is the percentage of shots that result in goals. It measures how efficiently a player or team converts chances into goals.

Full Explanation

Shooting percentage is one of the simplest but most important metrics in hockey analytics.

It is calculated as:

Goals ÷ Shots

If a player scores 10 goals on 100 shots, their shooting percentage is 10 percent.

This stat shows how effective a player or team is at finishing scoring chances.

However, it does not fully explain shot quality or context, which is why it must be interpreted carefully.

How Shooting Percentage Reflects Finishing Ability

Shooting percentage helps identify how well players convert chances.

Players with high shooting percentage usually:

Take shots from dangerous areas
Have strong finishing skills
Benefit from good offensive positioning

Teams with high shooting percentage often create better chances rather than just more shots.

NHL vs IIHF Context

Shooting percentage is used across all levels of hockey, including NHL and IIHF competitions.

However, values can vary depending on playing style, rink size, and level of competition.

The concept remains the same: efficiency in converting shots into goals.

Why Shooting Percentage Is Controversial

Shooting percentage is controversial because it can be heavily influenced by short-term variation.

Fans may assume a high shooting percentage means elite performance, but coaches understand that:

Hot streaks can inflate numbers
Cold streaks can suppress numbers
Sample size matters

This is why shooting percentage alone cannot fully define a player’s ability.

Edge Case: Extremely High Shooting Percentage

A player may have an unusually high shooting percentage over a short period.

This can happen when:

They score on a small number of shots
They benefit from high-quality chances
They experience favorable outcomes

In most cases, this level is difficult to sustain over time.

Numbers usually move toward a more typical range.

IHM Signal System: How to Read Shooting Percentage

To interpret shooting percentage correctly, focus on these signals:

Shot location: Are shots from dangerous areas?
Shot volume: Is sample size large enough?
Chance type: Rebounds, rush chances, slot shots
Consistency: Stable or fluctuating?

Trigger-level rule:

If shooting percentage is unusually high without strong shot quality, it is almost always unsustainable.

This is a key indicator of regression risk.

IHM Insight: Why Shooting Percentage Is Misunderstood

Shooting percentage is often misunderstood because it is treated as a pure skill metric.

In reality, it is influenced by both skill and variation.

Two players with similar ability can have very different shooting percentages over short periods.

Understanding this balance is critical for proper analysis.

Mini Q&A

What is shooting percentage?
Goals divided by shots.

Is higher always better?
Yes, but context matters.

Can it change quickly?
Yes, especially over small samples.

Does it show skill?
Partly, but not fully.

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

Why This Rule Exists

Shooting percentage exists to measure scoring efficiency and finishing ability.

It helps evaluate how well players and teams convert opportunities into goals.

Key Takeaways

Shooting percentage measures efficiency
It is goals divided by shots
High values may not be sustainable
Context and sample size matter
It should be combined with other metrics

What Is xG vs Actual Goals in Hockey? | IHM

IHM Knowledge Center

What Is xG vs Actual Goals in Hockey?

Why do teams sometimes create better chances but still score fewer goals, and what does that difference really mean?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

xG measures how many goals a team should have scored based on chance quality, while actual goals show what really happened. The difference reveals finishing ability, goaltending, and short-term variation.

Full Explanation

Expected goals (xG) and actual goals measure two different things.

xG is based on probability. It estimates how many goals should be scored based on shot quality.

Actual goals are the real outcomes on the scoreboard.

The gap between these two numbers helps explain performance.

If goals are higher than xG, the team is finishing above expectation
If goals are lower than xG, the team is underperforming relative to chance quality

This difference is one of the most important tools for understanding true performance versus results.

How Finishing and Goaltending Affect the Gap

The difference between xG and goals is mainly driven by two factors:

Shooting efficiency
Goaltending performance

A team with elite shooters may consistently score more than expected.

A team facing strong goaltending may score less than expected even with good chances.

This is why xG should always be analyzed together with actual results.

NHL vs IIHF Context

xG vs goals analysis is most common in the NHL due to detailed tracking models.

In IIHF tournaments, the same concept applies, but smaller sample sizes can create larger differences between expected and actual results.

The principle remains consistent across all levels.

Why xG vs Goals Is Controversial

This comparison is controversial because it challenges scoreboard results.

Fans often focus on goals only, while analytics suggest that underlying performance may tell a different story.

A team that wins with low xG may not have controlled the game.

A team that loses with high xG may have actually played better.

This creates a conflict between outcome-based thinking and process-based evaluation.

Edge Case: Extreme Overperformance

Sometimes a team scores far more goals than expected over a short period.

This can happen when:

Shooting percentage is unusually high
Opposing goaltending struggles
Small sample size amplifies results

In most cases, this level of overperformance is difficult to sustain.

Over time, results usually move closer to expected values.

IHM Signal System: How to Read xG vs Goals

To interpret the difference correctly, focus on these signals:

Chance quality: Are chances consistently dangerous?
Shot type: Rebounds, rush chances, slot shots
Goaltending: Is the goalie outperforming expectations?
Consistency: Short-term or long-term trend?

Trigger-level rule:

If a team consistently generates high xG but scores less, goals will almost always increase over time unless finishing quality remains poor.

This is a key indicator of future improvement.

IHM Insight: Why This Difference Matters

xG vs goals helps separate results from performance.

It shows whether a team is winning because of strong play or short-term factors.

This makes it one of the most powerful tools for predicting future trends.

Mini Q&A

What is xG?
Expected goals based on chance quality.

What are actual goals?
Real goals scored in the game.

Why are they different?
Because of finishing and goaltending.

Is higher xG always better?
Yes, over time.

Can a team win with low xG?
Yes, but it may not be sustainable.

Why This Rule Exists

This concept exists to evaluate performance beyond the scoreboard.

It helps analysts understand whether results match underlying play.

Key Takeaways

xG measures expected scoring
Goals show real results
The difference reveals performance vs outcome
Finishing and goaltending drive the gap
Trends matter more than single games

What Is Relative Corsi in Hockey? | IHM

IHM Knowledge Center

What Is Relative Corsi in Hockey?

How do analysts separate a player’s true impact from overall team performance when evaluating possession stats?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

Relative Corsi measures how a team performs in shot attempts when a player is on the ice compared to when that player is off the ice.

Full Explanation

Relative Corsi is an advanced version of the standard Corsi metric. Instead of just measuring total shot attempts, it compares a player’s on-ice performance to the team’s performance without that player.

This helps isolate individual impact.

The basic idea is:

On-ice Corsi shows how the team performs with the player
Off-ice Corsi shows how the team performs without the player
Relative Corsi is the difference between the two

If a player has a positive Relative Corsi, it means the team controls more shot attempts when that player is on the ice.

If it is negative, the team performs worse with that player.

How Relative Corsi Reflects Player Impact

Relative Corsi is used to measure whether a player actually improves team performance.

This is important because raw Corsi can be misleading.

A player on a strong team may have good numbers simply because the team is strong overall.

Relative Corsi corrects for this by comparing the player to their own team baseline.

This gives a clearer view of true individual contribution.

NHL vs IIHF Context

Relative Corsi is mainly used in NHL analytics where detailed player tracking data is available.

In IIHF competitions, it can still be applied, but the accuracy depends on the quality of available data.

The concept remains the same at all levels.

Why Relative Corsi Is Controversial

Relative Corsi is debated because it still depends on team context.

Fans may think it fully isolates the player, but coaches understand that:

Teammates still influence results
Matchups affect performance
Zone starts impact numbers

A player may have a negative Relative Corsi simply because they face top competition or start more shifts in the defensive zone.

This is why context remains critical.

Edge Case: Strong Player with Negative Relative Corsi

A player can have negative Relative Corsi despite being very effective.

This usually happens when:

The player is used in shutdown roles
They face top offensive lines
They start shifts in defensive situations

In these cases, weaker numbers do not necessarily mean poor performance.

Coaches often value these players highly despite the stats.

IHM Signal System: How to Read Relative Corsi

To interpret Relative Corsi correctly, focus on these signals:

Team strength: Is the team strong overall?
Role: Offensive or defensive deployment?
Matchups: Who is the player facing?
Zone starts: Offensive or defensive usage?
Consistency: Is the trend stable?

Trigger-level rule:

If a player consistently improves shot attempt differential compared to team baseline, their impact is almost always positive.

This is one of the clearest indicators of real influence.

IHM Insight: Why Relative Corsi Matters

Relative Corsi matters because it moves analysis from team performance to individual impact.

It helps identify players who drive play rather than just benefit from strong teammates.

This makes it one of the most useful tools for evaluating real value.

Mini Q&A

What is Relative Corsi?
Difference between on-ice and off-ice Corsi.

What does positive mean?
The team performs better with the player.

What does negative mean?
The team performs worse with the player.

Is it perfect?
No, context is still required.

Why use it?
To measure individual impact.

Why This Rule Exists

Relative Corsi exists to isolate player impact from overall team performance.

It provides a more accurate way to evaluate how much a player contributes to puck possession and pressure.

Key Takeaways

Relative Corsi compares on-ice vs off-ice performance
Positive values indicate strong impact
It helps isolate player contribution
Context is still important
It is widely used in advanced analytics

What Is Fenwick in Hockey? | IHM

IHM Knowledge Center

What Is Fenwick in Hockey?

How do analysts measure offensive pressure without counting blocked shots, and why is Fenwick used alongside Corsi?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

Fenwick is a statistic that counts unblocked shot attempts, including shots on goal and missed shots. It excludes blocked shots to focus on attempts that reach the net area.

Full Explanation

Fenwick is a key hockey analytics metric used to evaluate offensive pressure and puck possession, similar to Corsi but with one important difference.

It includes:

Shots on goal
Missed shots

It excludes:

Blocked shots

This makes Fenwick a more focused measure of attempts that actually travel toward the net and have the potential to create scoring situations.

How Fenwick Reflects Offensive Pressure

Fenwick is often used to measure how effectively a team generates shots that reach dangerous areas.

Since blocked shots are removed, Fenwick provides a clearer picture of:

Net-directed offense
Shot lanes being successfully created
Pressure that forces the goalie to react

This makes it slightly more connected to scoring potential than total shot attempt metrics.

NHL vs IIHF Context

Fenwick is widely used in NHL analytics due to detailed data tracking.

In IIHF competitions, the same concept applies, but tracking may be less consistent depending on data availability.

The principle remains unchanged across all levels.

Why Fenwick Is Controversial

Fenwick is debated because it removes blocked shots from analysis.

Some analysts argue that blocked shots still represent offensive pressure and should be counted.

Others believe that if a shot is blocked, it never becomes a real scoring threat and should not be included.

This creates a split between measuring total pressure and measuring effective pressure.

Edge Case: High Fenwick but Low Scoring

A team can have strong Fenwick numbers but still struggle to score.

This usually happens when:

Shots reach the net but are low quality
The goalie has clear visibility
There is little traffic or rebound presence

In this case, Fenwick shows offensive flow but not necessarily dangerous offense.

IHM Signal System: How to Read Fenwick

To interpret Fenwick correctly, focus on these signals:

Shot lane creation: Are shots getting through defenders?
Net pressure: Is the goalie being challenged?
Traffic: Is there screen presence?
Shot quality: Are attempts dangerous?

Trigger-level rule:

If Fenwick is high but high-danger chances remain low, offensive pressure is almost always inefficient.

This shows volume without real scoring threat.

IHM Insight: Why Fenwick Matters

Fenwick helps bridge the gap between raw shot volume and real offensive pressure.

It removes blocked shots to focus on attempts that actually reach the net area.

This makes it a useful complement to Corsi rather than a replacement.

Mini Q&A

What is Fenwick?
Unblocked shot attempts.

What is excluded?
Blocked shots.

Why exclude blocked shots?
They do not reach the net.

Is Fenwick better than Corsi?
It depends on what you want to measure.

What does high Fenwick mean?
Strong offensive pressure reaching the net.

Why This Rule Exists

Fenwick exists to provide a clearer measure of offensive pressure by focusing only on shots that reach the net area.

It helps analysts separate total activity from effective attacking play.

Key Takeaways

Fenwick counts unblocked shots
Blocked shots are excluded
It measures net-directed pressure
It complements Corsi
Context is still required

What Is the Difference Between Corsi and Fenwick?

IHM Knowledge Center

What Is the Difference Between Corsi and Fenwick?

Both Corsi and Fenwick measure shot attempts, but why do analysts separate them, and what does that difference actually show?

Editor: Coach Mark • Updated: April 26, 2026

Short Answer

Corsi counts all shot attempts, including blocked shots. Fenwick excludes blocked shots and only counts attempts that are not blocked.

Full Explanation

Corsi and Fenwick are closely related hockey analytics metrics used to measure puck possession and offensive pressure.

Both track shot attempts, but they differ in one key area: blocked shots.

Corsi: shots on goal + missed shots + blocked shots
Fenwick: shots on goal + missed shots

This difference changes how each stat is interpreted.

Corsi includes every attempt, which makes it a broader measure of total pressure and puck possession.

Fenwick removes blocked shots, focusing only on attempts that actually reach the net area or pass through defensive layers.

How the Difference Affects Analysis

The inclusion or exclusion of blocked shots changes what each stat represents.

Corsi reflects total offensive activity, including attempts that are stopped before reaching the net.

Fenwick focuses more on attempts that have a chance to become scoring opportunities.

This makes Fenwick slightly closer to measuring offensive effectiveness, while Corsi is more about overall puck control and pressure.

NHL vs IIHF Context

Both Corsi and Fenwick are primarily used in NHL analytics, where detailed tracking allows for accurate measurement.

In IIHF competitions, these metrics can still be applied, but their interpretation may vary depending on data availability and game style.

The core definitions remain the same across all levels of hockey.

Why This Difference Is Controversial

The difference between Corsi and Fenwick is debated because analysts disagree on the value of blocked shots.

Some argue that blocked shots still represent offensive pressure and should be included.

Others argue that a blocked shot does not test the goalie and should not be treated the same as an unblocked attempt.

This creates two perspectives:

Corsi values total pressure
Fenwick values potential scoring opportunity

Both views are valid depending on what you are trying to measure.

Edge Case: Teams That Block a High Number of Shots

An important edge case occurs when a team blocks a large number of shots.

In this situation:

Corsi may show strong offensive pressure from the attacking team
Fenwick may show lower offensive impact because many shots are blocked

This reveals something important about the defending team. It shows strong shot-blocking structure and commitment to protecting the net.

This is why using both metrics together provides better understanding than using only one.

IHM Signal System: How to Read Corsi vs Fenwick

To interpret these stats correctly, focus on these signals:

Shot suppression: Is the defense blocking many attempts?
Net access: Are shots reaching the goal area?
Pressure type: Volume pressure vs real scoring threat
System structure: Does the team allow outside shots?

Trigger-level rule:

If Corsi is high but Fenwick is significantly lower, the offense is almost always being neutralized by shot-blocking before reaching the net.

This indicates pressure without efficient scoring threat.

IHM Insight: Why This Difference Matters

The difference between Corsi and Fenwick helps explain how defense impacts offense.

A team may appear dominant in total shot attempts, but if many of those attempts are blocked, the real scoring pressure is reduced.

This is why advanced analysis often compares both metrics instead of relying on one.

Mini Q&A

What is the main difference between Corsi and Fenwick?
Blocked shots are included in Corsi but excluded in Fenwick.

Which stat is more accurate?
Both are useful for different purposes.

Why remove blocked shots?
Because they do not reach the net.

Does Corsi measure possession?
Yes, it is often used as a proxy for puck possession.

Should both be used together?
Yes, combining them gives better insight.

Why This Rule Exists

The distinction exists to separate total offensive pressure from attempts that actually reach the net area.

This allows analysts to better understand both possession and scoring potential.

Key Takeaways

Corsi includes all shot attempts
Fenwick excludes blocked shots
Corsi measures total pressure
Fenwick focuses on unblocked attempts
Using both gives better analysis

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.

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