Metrics for Flames Defensive Pairings

Metrics for Flames Defensive Pairings

For the dedicated fan or analyst, simply watching the Calgary Flames defend isn't enough. The true story of a pairing’s effectiveness—its synergy, its weaknesses, its impact on the game’s outcome—is written in data. In the modern National Hockey League, front offices like that of GM Craig Conroy leverage a suite of metrics to build and evaluate their blue line. For Head Coach Huska, these numbers inform deployment against top lines in the Pacific Division and critical matchups during the Battle of Alberta.

This guide provides a practical framework for analyzing Flames defensive pairings using publicly available advanced statistics. By the end, you will be able to move beyond basic plus/minus, identify which duos truly drive play, understand their deployment context, and assess their tangible impact on results. This is the same analytical process that shapes decisions at the Saddledome.

What You Need to Begin

Before diving into the numbers, gather these essential resources. Consistent access to them will transform your analysis from a snapshot into a compelling narrative.

Primary Data Hubs: Bookmark reputable public analytics sites such as Natural Stat Trick, MoneyPuck, and Evolving-Hockey. These platforms compile the raw data from the league’s play-by-play logs.
Contextual Knowledge: Have a foundational understanding of key metrics. We will define them in depth, but familiarity with terms like Corsi (shot attempt differential), Expected Goals (xG), and zone starts is beneficial.
The Right Lens: Always remember that defense is a five-player unit. A pairing’s metrics are influenced by the forwards in front of them and the performance of Jacob Markström behind them. Isolate the pairing’s contribution, but view it within the larger system.
A Focused Question: Are you evaluating a pairing’s overall viability, their shutdown capabilities, or their offensive contributions? Your question will determine which metrics you prioritize.

The Step-by-Step Evaluation Process

Follow this structured, four-step process to build a complete and nuanced profile of any Flames defensive duo.

Step 1: Establish the Foundation with Possession Metrics

The first principle of modern analysis is that you can’t score without the puck. Possession metrics tell us which pairings are tilting the ice in the Flames’ favor.

1. Identify the Pairing: Select the specific duo you want to analyze (e.g., Weegar-Andersson, Tanev-Hanifin).


2. Pull 5-on-5 Data: Filter your search on your chosen site to 5-on-5 play only. This removes the special teams noise and provides the cleanest view of even-strength performance, where defensemen play the majority of their minutes.


3. Analyze Corsi (CF%): This measures the percentage of all shot attempts (goals, shots on net, misses, blocks) taken by the Flames when this pairing is on the ice. A CF% above 50% means the Flames are controlling the attempt share. This is a primary indicator of territorial dominance.


4. Dig Deeper with Expected Goals (xGF%): This is the crucial next step. xGF% accounts for the quality of those shot attempts, weighting them based on location and type. A pairing with a strong CF% but a poor xGF% might be generating low-danger attempts while allowing high-danger chances against—a critical flaw.

> Key Insight: A strong top-pairing should consistently post a CF% and xGF% above 52-53%. For the Flames, a pairing excelling here is helping drive the play that forwards like Jonathan Huberdeau and Nazem Kadri thrive on.

Step 2: Contextualize with Deployment and Quality of Competition

Numbers in a vacuum are misleading. A pairing’s metrics must be weighed against the difficulty of their assignment.

1. Check Zone Start Percentage (ZS%): This shows the ratio of offensive zone faceoffs to defensive zone faceoffs a pairing receives. A ZS% above 50% indicates sheltered, offensive-minded deployment. A ZS% below 50% signifies a shutdown role, tasked with defending leads and starting in their own end.


2. Review Quality of Competition (QoC): While a nuanced stat, most sites offer a metric (often based on the opponent’s time-on-ice or possession numbers) showing the average caliber of forward line a pairing faces. Is this duo battling the other team’s stars or their bottom six?


3. Synthesize the Story: The most valuable pairings are often those with a low ZS% and a high QoC who still maintain break-even or positive possession numbers. They are given the hardest minutes and still hold their own. This is the hallmark of a true shutdown pair.

> Pro Application: This is where you appreciate the strategic mind of Head Coach Huska. He may deploy a rugged pairing in a pure defensive role (low ZS%, high QoC) to neutralize a top line, accepting lower possession stats if they prevent high-danger chances.

Step 3: Evaluate Defensive and Offensive Impact Separately

Now, break the pairing’s overall impact into its component parts to diagnose specific strengths and weaknesses.

For Defensive Rigor:
Expected Goals Against (xGA/60): How many goals should have been scored against the Flames per 60 minutes with this pairing deployed? A low number is excellent.
High-Danger Chances Against (HDCA/60): The rate of shots from the most dangerous areas on the ice (slot, crease). This directly tests a pairing’s gap control, net-front clearance, and structural discipline.
On-Ice Save Percentage (SV%): Compare the goaltender’s save percentage when this pairing is on the ice to the team average. A significantly lower SV% can indicate the pairing is allowing a high rate of deceptive shots or screens, though sometimes it’s just poor luck.

For Offensive Catalyst:
Expected Goals For (xGF/60): The quality of chances the pairing helps generate.
On-Ice Shooting Percentage (SH%): Similar to SV%, a very high or low number here can signal puck luck. A pairing with strong xGF but low actual goals may be due to cold finishing from forwards like Connor Zary or Yegor Sharangovich.
Primary Points/60: A direct measure of offensive contribution. For a pairing like Weegar-Andersson, this metric highlights their dual-threat capability.

Step 4: Synthesize and Apply to the Flames' Season

The final step is to bring your analysis together to form a actionable conclusion about the pairing’s role and value.

1. Profile the Pairing: Based on Steps 1-3, label them. Are they a Shutdown Pair (low ZS, tough QoC, strong defensive metrics)? A Two-Way Driver (balanced ZS, strong CF%/xGF%)? Or an Offensive Shelter (high ZS, weaker competition)?


2. Assess Fit for Purpose: Does their statistical profile match their perceived role? Is a pairing being used in a role its metrics suggest it cannot sustain?


3. Project Sustainability: Look for outliers. Is a pairing’s success buoyed by an unsustainably high on-ice SH% or SV%? These numbers often regress to the mean over time.


4. Apply to Team Context: How does this pairing’s performance affect the Flames’ outlook? For instance, a stable, defensively sound second pairing is what allows the top pairing to take more offensive risks. Their performance is a key variable in the team’s success in the Western Conference.

Pro Tips and Common Mistakes to Avoid

Tip: Use Relativity. Always compare a pairing’s metrics to other Flames pairings first, then to league averages. This tells you if they are a strength or weakness within the team’s construct.
Tip: Watch the Games. Use metrics to guide your viewing. If the numbers say a pairing is struggling defensively, watch their shifts closely. Are they losing board battles? Is their gap control poor? Let the data ask the questions, and the video provide the answers.
Mistake: Ignoring Sample Size. Evaluating a pairing after 5 games is folly. Look for trends over a minimum of 20-25 games in the 2023-24 NHL season before drawing firm conclusions. Injuries and lineup changes also disrupt chemistry.
Mistake: Overvaluing Plus/Minus. This traditional stat is heavily influenced by goaltending and teammate shooting percentage—factors largely outside a defender’s control. It is an outcome stat, not a process stat. Rely on xGF% instead.
* Mistake: Forgetting the Human Element. Metrics won’t capture leadership, mentorship of a young partner, or the intangible confidence they provide to Jacob Markström. Use stats to inform your opinion, not replace hockey sense.

Your Defensive Pairing Analysis Checklist

Use this bulleted list to ensure you’ve conducted a thorough evaluation of any Calgary Flames defensive pairing.

• Gathered 5-on-5 data from a primary analytics site.


• Calculated and interpreted core possession metrics: CF% and xGF%.


• Contextualized deployment by checking Zone Start Percentage (ZS%).


• Considered the Quality of Competition (QoC) they regularly face.


• Evaluated defensive impact via xGA/60 and High-Danger Chances Against/60.


• Evaluated offensive impact via xGF/60 and Primary Points/60.


• Noted any potential puck luck outliers in On-Ice SV% and SH%.


• Synthesized findings to profile the pairing’s role (Shutdown, Two-Way, Offensive).


• Assessed if their statistical profile aligns with their on-ice role and minutes.


• Applied the analysis to the broader context of the Flames’ team needs and goals.

By applying this framework, you move past the surface and into the nuanced reality of how the Flames’ blue line is constructed and performing. This analytical approach mirrors the processes inside the Dome, giving you a deeper, more authoritative understanding of the team’s fortunes. For further deep dives, explore our hubs on Flames Goalie SV% & GAA Advanced Stats and how Flames Shot Location Heat Maps reveal systemic strengths and weaknesses. All this and more is part of our continuous Flames Stats & Metrics Analysis.