Introduction to Advanced Analytics

By JASON LOWENTHAL

With antiquated measures of statistics such as plus/minus becoming less and less relevant, advanced analytics have become enamored by the modern world of hockey. Former Buffalo Sabres goaltending coach Jim Corsi, who aimed to expand possession metrics in hockey, primarily developed advanced analytics, or “fancy stats”. Corsi is measured using the following formula:

Corsi = shots on goal + blocked shots + missed shots

Fenwick is an adaptation of Corsi and is typically considered a more accurate indicator. It is calculated with the formula below:

Fenwick = shots on goal + missed shots

While Corsi and Fenwick are perhaps the most common metrics in advanced analytics, one lesser-known stat is Individual Points Percentage (IPP). IPP is the percentage of goals scored while a player was on the ice that the player had a point on. IPP is found with the following equation:

IPP = (goals + assists) / total goals for scored while player is on ice

Essentially, IPP values a player’s offensive production while that player is on the ice. Take former Chicago Steel forward CJ Smith, for example. Smith has moved on to play his college hockey at the University of Massachusetts-Lowell, but led the Steel in IPP last season. During his 46 games with Chicago last year, Smith recorded 23 goals, 17 assists, and was on the ice for 52 Steel goals. Therefore,

IPP (Smith) = (23 + 17) / 52

IPP (Smith) = .769

This means that Smith registered a point on 76.9% of the goals scored while he was on the ice for Chicago last season.

Bringing IPP to the NHL-level, Taylor Hall of the Edmonton Oilers is a master of IPP. Since jumping onto the scene during the 2010-11 season, Hall has increased his IPP each season. Hall is so equipped with his offense and his line’s production that his IPP is nearly as high as it could ever go. Last season, Hall scored 16 goals and added 37 assists. He was on the ice for 54 Oiler goals. Therefore, his IPP is an astounding .981. Meaning, Hall was responsible for either scoring or assisting on 98.1% of the goals scored while he was on the ice.

For the Chicago Blackhawks, Patrick Kane is the leader of IPP and has been steady at that. Since joining the ‘Hawks, Kane has maintained an IPP between .750 and .828. This shows how integral Kane has been throughout the years to his line’s success.

With the 2014-15 season rapidly approaching, we can apply IPP to returning players for the Steel. Out of returning forwards, Michael Booth and John Schilling led the way last season with IPP’s of .667 and .634, respectively. On the defensive side, the Steel lost their top three IPP leaders, but returners Jake Bunz (IPP: .350) and Liam McGing (IPP: .350) look to pick up the slack. For the full list of IPP’s for returning Steel players, check out the table below:

Name	Goals	Assists	Goals scored while on ice	IPP
Connor Yau (D)	2	12	45	.311
Jake Bunz (D)	3	4	20	.350
Liam McGing (D)	0	7	20	.350
Nate Kwiecinski (D)	1	4	33	.152
Peter Tischke  (D)	0	9	37	.243
Brendon Kearney (F)	1	18	35	.543
Mason Bergh (F)	11	14	25	.610
John Schilling (F)	10	16	41	.634
Freddy Olofsson (F)	4	11	26	.577
John Ernsting (F)	12	29	68	.603
Brady Jones (F)	1	1	6	.333
Robby Jackson (F)	28	14	67	.627
Connor McDonald (F)	1	11	49	.245
Michael Booth (F)	1	3	6	.667

IPP is a quality indicator for pinpointing which players drive play in the offensive zone. However, it is by no means the lone measure of offensive control. United States Hockey League rookie of the year Robby Jackson finished only seventh on the team last season with an IPP of .627. Though, despite its imperfections, IPP does provide an indication for which individuals are integral to a line’s success. It’s not to say that IPP is the future of the hockey analytics world, but it is no doubt a useful statistic for head coaches and general managers across the board.