Florida Panthers’ Consistency Brings Analytics Conversation to Light

2015: Analytics’ Introduction to Florida

By the end of 2015-16, the Panthers were not considered a success by any stretch. In their previous 18 seasons, they were 573-641-204, winning just over 40% of their games and appearing in the playoffs only three times (16.6%).

Brian Macdonald, a 2000 graduate of Lafayette College in Pennsylvania, was hired by the Panthers as their first Director of Hockey Analytics. He became a major piece in kickstarting analytical thinking processes within their organization.

“There’s no substitution for watching a game, but you can’t watch every minute and every shift,” he told Lafayette.edu in an interview. “These stats can make you more aware of the aspects of a particular player’s game and of a team’s strengths and weaknesses. It’s an additional tool for the toolbox.”

Analytics Continue to Grow

Macdonald left after 2017-18, but the Panthers continued to improve their focus on analytics as they started to pick up on bits and pieces of how certain data could improve on-ice results. In 2020, they brought aboard Sunny Mehta, who is now considered one of the greatest analytical minds in sports. He previously started the NHL’s first full-time analytic department with the New Jersey Devils, before consulting for the Washington Capitals and six Major League Baseball teams. He was promoted to Assistant General Manager ahead of 2023-24, while simultaneously getting promoted from ‘Analyst’ to ‘Director of Analytics’.

Since Mehta was hired, they’ve played to a remarkable 236-119-29 record. Much of that goes to bringing along talents like Sam Bennett and Gustav Forsling, who caught the Panthers’ eyes with promising underlying numbers on their previous teams. The Panthers fine-tuned some things, working with them closely to turn them into elite players.

In an interview with NHL.com, Panthers’ general manager Bill Zito said, “Professionally, I think there’s a lot of bright people, but (Mehta) has an uncanny ability to digest incredible amounts of information, rapidly regurgitate them, simplify them, and then project them back out to whoever his audience is. That’s a remarkable skill there.”

In both Macdonald and Mehta’s philosophies, there’s a notable sentiment in that analytics need to be properly communicated to the players in order to be effective. It’s not as simple as some Ivy League graduate throwing a chart and a bunch of numbers at someone, and bam, success.

Former Devils and current Ottawa Senators head coach Travis Green shared a similar thought when The Hockey Writers asked him about analytics in 2024:

“I don’t think the players are involved with analytics as much as coaches. I do believe if you involve analytics into the players and talk about it with them, then it can overload them. I think the analytics are a byproduct of coaching…there’s so many ways you can use them. Even practice – the level of pace that you need to practice at. It’s a great tool for coaches. I thoroughly believe in it.”

– Travis Green

A Common Misconception with Analytics

An issue with the emergence of analytics is that their effectiveness isn’t always conveyed simply enough to fans. In the world of social media, any self-proclaimed expert can throw up a graphic with a bunch of fancy symbols and formulas and say that ‘X’ hockey team needs more or less of ‘Y’. But in reality, the average fan (understandably) does not want to be bothered with trying to make sense of these.

As Zito alluded to, just because someone is intelligent doesn’t mean they have the skillset to convey the information to people unfamiliar with a concept. For example, Natural Stat Trick has tons of data available to the public, all with abbreviations (Corsi for – CF, etc.). The average fan would come across an internet debate regarding CF and likely be confused, but Corsi is simply shot attempts.

In the playoffs, Forsling has an on-ice CF of 370, with a Corsi against (CA) of 309. All that means is that the Panthers have 61 more shot attempts than the opposition when he’s been on the ice. It doesn’t need to be made more complicated than that.

If a player has a shootout percentage that’s 18% higher than the next closest teammate, that’s analytics. That decision can be the difference between winning and losing a key game. If a goalie gives up more goals through the five-hole than anywhere else, that’s analytics. If a skater produces 30% more points on the left wing compared to right, you guessed it: analytics. For as confusing as some graphs and formulas may appear to be, they aren’t normally having direct impacts on the game.

The analytics used by organizations, at least at the coaching level, are much simpler. The more detailed stuff is kept to the front office when making longer-term decisions. In fact, in 2024 when The Hockey Writers asked former Devils netminder Kaapo Kahkonen about his stellar high-danger save percentage – an advanced goalie stat constantly talked about on social media – he didn’t even know what it was!

But that’s probably a good thing. A great coaching staff, like the Panthers, will tell their player to keep doing what they’re doing if the analytics back up their success. There’s no need to directly bring up the numbers and overwhelm a player with information.

Important Tool, But Not “The” Sole Answer

Another important factor in the Panthers’ success is that analytics aren’t always the answer; the eye test and having a feel for the game are also important. Data can’t always 100% accurately reflect what’s going on in front of your two eyes, especially in hockey where analytics are not as advanced as basketball, baseball, etc.

Momentum and intimidation aren’t accurately measured, but they can certainly be real. Over a large sample, analytics can help make decisions. But in the shorter term, they can’t always directly take into account extenuating circumstances (player personal issues, illness/injury) and when they do, it’s hard to able to quantify the exact impact that specific factor has in their play. Remember: these are human beings out on the ice, not robots.

Oftentimes, “analytics darlings” can falter in the biggest moments, even when the numbers say they should succeed. Take Connor Hellebuyck for example, who had an incredible .925 save percentage (SV%) and 39.6 goals saved above expected (GSAx – per MoneyPuck) this season. But in the playoffs, he had an .866 SV% with minus-4.8 GSAx.

This is precisely why having a top-tier coaching/training staff with a feel for the game is vital, something the Panthers organization takes a ton of pride in. As head coach Paul Maurice said on the Jun. 4 edition of 32 Thoughts, “We’re like a family.”

He then emphasized how many players, like A.J. Greer, have mentioned how the Panthers’ organization is better at connecting with players and setting them up for success than any previous organization he’s been with. The ability to effectively communicate analytics with the players certainly plays a role there.

“Gotcha Moments”

Another obstacle in analytics conversation is “gotcha moments”. This is when a team makes an educated decision, for example, opting for their backup goalie, who has been lights out, over a struggling starter in a big game. If the team doesn’t win, that decision gets heavily scrutinized, especially by fans who are vehemently anti-analytics.

But the job of an analytics department is to give a team the best chance to succeed. That never means it’s a guaranteed chance. Like anything else, there’s risks in these decisions. Sports always will be unpredictable by nature. That doesn’t mean when a decision goes wrong, the process is wrong in of itself.

Specific Analytics in Today’s Florida Panthers

Today, the Panthers have four Data-Driven employment positions filled. Obviously, more doesn’t always equal better, but eight teams only had one such position two seasons ago, while the Panthers were then tied for second-most. They were always ahead of the game.

Now, every team except the Washington Capitals and Vegas Golden Knights have at least three positions filled. There’s a reason most teams are scrambling to improve.

In the last two seasons, the Panthers have a record of 99-55-10. During that span, they’re a top three team in expected goals percentage (xGF%) and scoring chances against. On 32 Thoughts, Maurice said, “Analytically, we’ll tell you, we’ve been a pretty good defensive team. (We were) number one last year in the playoffs.”

Interestingly enough, they didn’t lead in goals against per game, in fact, five playoff teams were better in that category. So what did he mean by “number one”? Well, it’s hard to know for sure, but they did lead in xGF%.

A common retort to “expected goals” chatter is, “Who cares if the goals were expected; shouldn‘t actual goals be the only thing that matters!?”

That logic makes sense in a vacuum, but it diminishes the fact that certain team’s performances are lucky rather than good, which often gets corrected come playoff time. Over the last three seasons, just two out of the 44 teams to have a sub-50 xGF% in the regular season have won a playoff series. Only the 2023-24 New York Rangers advanced past the second round; they made it to the Eastern Conference Finals where the Panthers beat them in six.

Moving Forward

If you’ve made it to this point, I will offer some advice. If you’re pro analytics, try to open your mind to those who aren’t and at least understand why some are turned off by it, even if you don’t agree.

If you’re anti-analytics, try to read up a little more and understand that the “nerds” have successfully played roles in pro sports franchises dating back years, and all they’re there to do is give teams the best chance night in and night out. They aren’t implementing anything that isn’t backed by coaches and high-ranking executives with decades of experience around the NHL.