Advanced Analytics Part IV

By JASON LOWENTHAL

Today we conclude our series on advanced analytics in hockey by putting it all together. Remember back in our first piece when we introduced Corsi and Fenwick? Let’s go back to that.

Corsi = shots on goal + blocked shots + missed shots

                                      Fenwick = shots on goal + missed shots

Generally, Fenwick is considered to be a more accurate indicator, so we’ll stick with that as our baseline possession metric. The reason is because Fenwick does not include blocked shots in the equation because it considers blocking shots a skill, whereas Corsi does not.

Fenwick is used as a way to expand possession statistics in hockey because calculating time of possession is incredibly difficult. A team can’t have 20 different guys watching each of the 20 players that dress every game. That costs time and money. So, we use Fenwick instead. Here’s an example of Fenwick using Jonathan Toews during five-on-five play.

iFenwick (Toews) = shots on goal + missed shots

iFenwick (Toews) = 141 shots + 33 missed shots

iFenwick (Toews) = 174

(iFenwick stands for Individual Fenwick)

Now that we have Fenwick down, we can take a look at Fenwick For percentage. FF% can be used both individually and for a team, and is probably the best metric that advanced analytics has to show its validity. FF% is calculated by using the following formula:

FF% = (100 x Fenwick For) / (Fenwick For + Fenwick Against)

Essentially, FF% is used to measure the percentage of shots a team has in a game to show possession. Here was the FF% for the Chicago Blackhawks last season:

FF% (Blackhawks) = (100 x FF) / (FF + FA)

FF% (Blackhawks) = (100 x 1565) / (1565 + 1285)

FF% (Blackhawks) = 156,500 / 2850

FF% (Blackhawks) = 54.91

This means that during all of their games last season, the Blackhawks had 54.91 percent of the shots (on goal and missed).

The table below shows the top ten teams from last season in the NHL for FF%:

Los Angeles Kings	56.1%
Chicago Blackhawks	55.4%
San Jose Sharks	54.6%
St. Louis Blues	53.7%
New Jersey Devils	53.6%
Boston Bruins	53.4%
New York Rangers	52.6%
Vancouver Canucks	51.6%
Detroit Red Wings	51.5%
Tampa Bay Lightning	51.3%

Green indicates team made playoffs

Red indicates team missed playoffs

As you can see, eight of the top ten teams in terms of FF% ended up making the playoffs last season. This gives significant validity to FF% to the use of advanced statistics in hockey in general.

Unfortunately, given the limited budget of USHL teams, advanced analytics are hard to come by in the league. However, as one can see from this four-part series, it can be done.

Advanced statistics are a good way for guys doing the little things to be recognized. However, it will never surpass the eye test. Although more and more teams are moving towards the whole “Moneypuck” idea, a combination of the two theories is best, because both have imperfections. Advanced stats worked for Billy Beane and the Oakland A’s, time will only tell if the same will work in hockey.

Goals Created and Points Per Shot Attempted

By JASON LOWENTHAL

Photo Credit: MJB Images

In our third installment on advanced analytics, we take a look at one measure of scoring production and one measure of scoring efficiency: goals created and points per shot attempted, respectively.

The goals created statistic was developed by the fine folks over at Hockey Reference and is used to measure scoring production by giving separate value to goals and assists. Currently, when measuring scoring production, we use the total points system (goals + assists). Although a spectacular pass may have set up the goal, most people argue that goals are more valuable. Therefore, the goals created metric gives one “point” for every goal scored, and one-half “points” for each assist. The entire formula looks like this:

Goals created (GC) = (goals + (0.5 x assists)) x (team goals / (team goals + (0.5 x team assists))

Now, the formula to figure out goals created obviously looks a little complicated. So, we’ll break it down into steps. Let’s first looks at the second half of the formula, the one with all the team-oriented numbers. Last season, the Steel scored 179 goals and had 263 assists as a team. Now we plug those numbers into the equation:

Team goals / (team goals + (0.5 x team assists)

179 / (179 + (0.5 x 263)

179 / (179 + 131.5)

.57648953

That number that we ended up with (.576) will be used to find the goals created of all the Steel players last season. We only need to figure out the “team” part of the equation one time. Now, we can move on to players.

Leading the team last season in scoring was C.J. Smith with 45 points. To reach those 45 points, he registered 27 goals and 18 assists. Therefore, the “player” part of the formula looks like this for Smith:

Goals + (0.5 x assists)

27 + (0.5 x 18)

36

By multiplying the two parts of the formula, we get one final number, goals created:

Goals created (Smith) = 36 x .57648953

Goals created (Smith) = 20.75

It just so happens that Smith was the team leader in points as well as goals created, although this is not always the case. For instance, Danny Fetzer ranked just ninth on the team last year in points, but finished tied for fifth in goals created. This is because he had more goals (16) than a handful of guys above him. Alec Vanko (9 goals), John Schilling (10), Robbie Payne (14), and Mason Bergh (11) all shared the puck more, which is not a bad thing, that is the reason why their goals created were less than Fetzer’s.

Simply put, goals created is just another scoring metric, but is one that gives a higher value to goals than assists. The one criticism is that the statistic benefits those who played in more games than others. Obviously, if a skater is on the ice more, he has a better chance of scoring than if he is on the bench.

Now we turn to scoring efficiency, which we will measure using points per shot attempted. Quality shot selection is one component that will help one’s point per shot attempted. If a player is firing from bad angles and allowing the goalie to make routine saves, their points per shot attempted will suffer. The formula is very easy. All you have to do is divide the player’s total points by their total number of shots. Last season’s overall leader on the Steel was John Ernsting (12 G, 29 A). Ernsting tallied 41 points on the season and registered 119 shots on net. Therefore, his points per shot attempted is:

Points per shot attempted (Ernsting) = points / shots

Points per shot attempted (Ernsting) = 41 / 119

Points per shot attempted (Ernsting) = .34

Trailing right behind Ernsting and leading Steel defensemen was Brian LeBlanc at .33 points per shot attempted. This means that both Ernsting and LeBlanc were responsible for approximately one-third of a point for every shot they each had last season. Or, if you want to look at it the other way, for every three shots Ernsting or LeBlanc had, they were responsible for one point.

Jackson Catching Fire in Ivan Hlinka Memorial Cup

By JASON LOWENTHAL

Photo Credit: MJB Images

Over in Breclav, Czech Republic and Piestany, Slovakia, the Chicago Steel are proud to have two players representing Team USA in competition during the Ivan Hlinka Memorial Cup.

Reigning United States Hockey League rookie of the year Robby Jackson has posted impressive numbers thus far, ranking fourth on the team and ninth overall in scoring. Jackson has registered two goals and two assists in three games. In the opener, he found the net on a penalty shot in a 4-2 loss to the Czech Republic. After being held pointless in a 7-4 come-from-behind victory over Russia, Jackson had his best game of the tournament to date against Finland to put the United States into the semifinals. Jackson scored a goal and had two helpers in the convincing 9-4 win to set up a semifinal matchup with Canada.

Meanwhile, Tanner Laczynski is also enjoying his first international experience. The 17-year-old forward has appeared in all three games for Team USA, but has been held scoreless. However, he has picked up two penalty minutes. Last season, Laczynski spent two games with the Steel, three games with Chicago Mission U-18, and the majority of his time with Chicago Mission U-16.

Canada and the United States will face off on Friday for the right to play the winner of the Czech Republic and Sweden in the championship.

Hitting the Ice: Steel Head Coach Accepts ALS Ice Bucket Challenge

By JASON LOWENTHAL

Since the beginning of August, the ALS Ice Bucket Challenge has swept the nation, raising over $4 million for the ALS Association and its chapters. In the challenge, one has to have a bucket of ice water dumped over his or her head and then nominates other friends to do the same within a 24-hour period. Each time the ice bucket challenge is completed, a donation is made to help fight ALS, also commonly known as Lou Gehrig’s Disease. Your Chicago Steel have gotten involved in the challenge as well!

Coach McConnell was nominated for the challenge and bravely accepted and completed it earlier today. McConnell decided to pass on the challenge to defenseman Jake Bunz, forward Robby Jackson, and former Steel coach Steve Poapst.

To get in on the action and help fight ALS with the Steel, use the hashtags #IceBucketChallenge and #ALS and help spread awareness across social media!

Quality Starts, Quality Start Percentage and Goalie Steals

By JASON LOWENTHAL

Our second installment of advanced statistics focuses on goalies. In basic hockey stats, we use wins, goals against average, and save percentage as baseline metrics to value a goaltender. Today we take a look at more advanced goaltending metrics; quality starts, quality start percentage and goaltender steals.

A quality start, as defined by Hockey Prospectus, is a statistic used to measure whether a goaltender “gave his team a chance to win.” Hockey Prospectus also acknowledges that this statistic is directly borrowed from the game of baseball for pitchers. To determine whether a goalie’s effort was worthy of a quality start, his stat line must land within one of the following options:

·         Allowed two goals or less AND save percentage of at least .844

            ·         Allowed three or more goals AND save percentage of at least .911

Let’s apply this to a couple games from last season so we can see the difference between a quality start and a non-quality start. In the season opener last year against Green Bay, Steel goaltender Chris Nell surrendered five goals on 34 shots. So, we look at our options and determine that Nell must have recorded a save percentage of at least .911 for his effort to be determined as a quality start. However, his save percentage for the game was only .853. Thus, this was not a quality start in the season opener. Now look at his next performance. The following day against Waterloo, Nell was back in net and he allowed three goals on 41 shots. Again, we look to the second option because he allowed three goals. However, this time, his save percentage was .927. Because .927 is larger than .911, this qualified as a quality start for Nell. One important note is that even though the Steel lost this game to Waterloo by a score of 3-2, a quality start was still recorded. The outcome of the game has no effect on determining whether a start is considered quality or not.

Through the course of the season, Nell started 40 games. In those starts, 20 were quality, giving Nell a quality start percentage of .500 for the season. His backup, Cale Morris, started the remaining 20 games and recorded 11 quality starts, giving him a quality start percentage .550. Morris’ quality start percentage was slightly higher than Nell’s last season, but Nell started in net more often. A quality start percentage of .600 is considered very good for a goaltender and a quality start percentage of .400 is poor. Therefore, Morris is placed in the upper echelon of goaltenders in terms of quality start percentage while Nell is considered more average, in the middle.

Looking at the NHL gives a good amount of validity to quality start percentage. Last season, four of the top five leaders in quality start percentage (min. 41 games started) were also the top four candidates for the Vezina Trophy. The lone exception was Devils goaltender Cory Schneider, who finished third in quality start percentage at .651. Fourth in the league was Ben Bishop of the Lightning at .635. Vezina-winner Tuukka Rask (.690) finished second on the list and Avalanche netminder Semyon Varlamov led the league in quality start percentage at .733. The goaltending statistics for the 2013-14 NHL season were obtained from Hockey Abstract.

Essentially, quality start percentage gives value to the percentage of games started in which the goaltender kept his team in the game. It can also be used as a measure of consistency for a goaltender.

The other advanced goaltending statistic we will take a look at it goalie steals, defined as a game in which the goalie surrenders one goal or less while facing at least 35 shots.

Last season, Nell was awarded with four goalie steals. His most impressive performance statistically came in a 3-0 shutout of Fargo when he turned away all 40 shots. Morris failed to qualify for a goalie steal last season.

Obviously, the problem with goalie steals is that it cannot be used as a measure of consistency. Therefore it does not have as much validity as quality start percentage. However, it still has some value as a measure of the amount of games that the goalie single-handedly kept his team in (stole). Typically, this comes in a victory, although it does not have to. For instance, Nell recorded a goalie steal in a 1-0 loss to Dubuque last year when he stopped 34 of 35 shots.

Be sure to check out our third installment of advanced statistics when we head back to the offensive end for a look at goals created and points per shot attempted.

Future Players Make Memories at Steel Youth Hockey Camp

By JASON LOWENTHAL

Part of the mission of the Chicago Steel organization is giving back to the youth hockey community and helping all levels grow. One of the more popular events to reach this goal is the Youth Hockey Camp directed by Steel coaches, players, and front office staff. In its seventh year, the Youth Hockey Camp is designed to develop the hockey skills of aspiring youth players.

“The Youth Hockey Camp definitely keeps our brand out there and the image that we want to uphold in the community where we’re helping develop skills,” said Steel Director of Business Operations John Montalbano. “That’s the whole goal of the Chicago Steel and the league itself is to develop the skills of the players.”

The camp is run Aug. 5-8 and offers a full day (8:30 A.M. – 5:00 P.M.) of activities. Kids are on the ice for at least two hours each day with their coaches and players. In addition to getting top quality on-ice instruction, the camp allows kids to get out into the sun and participate in a variety of activities to help develop coordination skills. Kickball, soccer, and wiffle ball are just a handful of the games kids get to play. For second-year center John Ernsting, it is an interesting experience acting as a coach instead of a player.

“It’s a lot different,” said Ernsting. “It’s fun to attract kids to the Steel and have them look up to us. They get to learn from people that have been in their position before.”

“It feels good giving back to the younger kids and helping them try to get to the level we’re at right now,” added defenseman Connor Yau. “They’re getting better at hockey being able to get experience and have fun with us and the coaches.”

On the final day of the camp, kids will conclude their on and off-ice games and will also have a barbeque to look forward to. In addition, kids receive a camp jersey and a Steel Prize Pack.

While the Steel Youth Hockey Camp is one of the bigger events of the year, the Steel also give back to the community in many other ways.

“We try to get to community events at least once a week,” said Montalbano. “In the summer we do fairs and festivals where we’re out there with our logo.”

One event during the season is “Project Care,” in which players go to classes and work with students each week on anything ranging from learning how to read, to math flash cards, to instructing gym class.

“With our players going into youth hockey communities throughout the year, that keeps the exposure there and allows us to grow as an organization,” said Montalbano.

For more information or to contact the Chicago Steel about coming to your community, please visit chicagosteelhockeyteam.com.

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.