Meier, according to a story being discussed on Sportsnet was almost in the opposite uniform of the two Western Conference finalists, when they almost consummated a deal that would have sent a 1st round pick – the selection that became Timo Meier – traded to the St. Louis Blues along with Tomas Hertl for Kevin Shattenkirk and T.J. Oshie.
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Meier and Schwartz are one and two in scoring thus far in the postseason at 5v5, and Nyquist is third overall with 10 points. There’s a distinction between the Sharks players and the lone Blues forward in the data.
Individual point percentage (IPP). The metric accounts for the percentage of points earned on goals scored while on the ice. The table below illustrates how they stack up.
Meier and Nyquist have contributed significantly to the scoring while on the ice, firing a fairly respectable individual shooting percentages of 9.3% and 7.14% respectively.
Meier, in particular, is etching a playoff masterpiece. When presenting his individual and on-ice metrics, on a per-60 basis, he leads in every respectable category – despite slightly lagging his expected goals/60 of 1.26 by scoring at a 1.04 rate.
A note on the Sharks deployment on the ice; check out the highlighted area in the medium danger chances for column (MDCF). Other than the Meier/Couture/Nyquist trio, defensemen Erik Karlsson and Brent Burns lead the team. They do a good job of getting into more dangerous areas than just off the point to fire shots and create plays, giving the Sharks a deadly pairing when desperation kicks in for a goal.
Schwartz, conversely, had to elicit a bloated 26.7% shooting percentage to fire eight of the 15 goals scored while on the ice. He’s outperformed his xG of 2.39, heavily influenced by shooting percentage, while earning a point on 73.3% of the goals scored, well below his Sharks contemporaries IPP. The 15 on-ice goals with Schwartz on, is significantly higher than the 8.28 expected goals (xGF). These elements are diametrically opposed, so while Meier has the potential to keep rolling at the same pace, Schwartz may inevitably slow down as he regresses to the mean.
Take a good look at Robert Thomas. The former Hamilton Bulldog spark plug has impressed all season long, scoring nine goals, three via 5v4, and 33 points in 73 games. But this postseason he leads the club with high danger scoring chances, individual scoring chances – even if he isn’t playing a major role.
I’m focusing here on 5v5 because both the Western Conference Finalists scored well at even strength during Round 2. The Sharks didn’t exactly light it up at evens in Round 1 and a reason for a long seven-game series – that four power play goal outburst will live on in infamy. In Round 2, they bested their xG of 14.09 by scoring 16 (Colorado 15.09 xG, 14 goals) – without Joe Pavelski, who after a pointless Round 1 versus Vegas scored his only two points of the playoffs in Game 7 versus Colorado.
The Blues matched their 10 expected goals by scoring 10 versus the Winnipeg Jets and scored 15 versus Dallas (13.77 xGF) with at least one 5v5 goal in each game. The only game without a 5v5 goal was Game 4 versus the Jets, otherwise, they’ve received consistent scoring. If there’s one concerning spot for the Blues, it’s the absence of Vladimir Tarasenko at even strength. The Russian has scored once at 5v5, and added two assists, in 230 minutes, clearly not enough out of a star of his caliber. In the previous image, Tarasenko and David Perron lead the team with scoring chances from low danger areas, unacceptable for skilled players. For the Blues to maintain success, this dynamic is going to have to change and they’ll have to get into dirtier areas to induce scoring.
I’m linking to data in today’s post by Natural Stat Trick and with the data I’ve created this workbook which can be accessed and downloaded here. The workbook contains pivot tables for key performance metrics, based on the combination of individual and on-ice stats at 5v5 and some at 5v4. Raw and per 60 minutes stats are in separate tabs.
Working with analytics requires some distinct skills to analyze and interpret data, and some skills to manipulate some of the data in a spreadsheet. Some readers have the interpretation skills, but may not have the background coding, or capability to maneuver the data for analysis.
Hopefully, by providing this workbook to the public, in an already preformatted version can further the hobbyist analyst, without requiring any extra skills development.
But, if you’re going to work with data, it’s best to understand the tools and capabilities. Begin with Microsoft Excel, and learn some basic formulas, then apply some macros/VBA code, before moving on to other platforms like R. Ultimately, Python users have a distinct advantage over other data analysts for using big data.
I hope sharing this workbook offers some value.