List of Figures Introduction The PDF Document The Skater Spreadsheet The Goalie Spreadsheet...

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2 Contents List of Figures 23 1 How To Use This Guide Introduction The PDF Document The Skater Spreadsheet The Goalie Spreadsheet Shooting Percentage as a Tool Introduction A Quick Discussion on Coin Flips Applying Coin Flip Experiments to NHL Players Corey Perry s 50 Goal Season Lessons From the Draft Kit Using SH% in Your 2014 Fantasy Draft Using SH% in Your 2014 Fantasy Season Claude Giroux: Joe Pavelski: Phil Kessel:

3 CONTENTS Can it be Used in Reverse? Phil Kessel: season Penalty Kill Save Percentage Background PKSV% Is Non-Repeatable Implications Is PKSV% Predictive? Data Final Thoughts Projecting A Goalie s Save Percentage Background The Method Introduction Looking at the League Averages How to Project When Are We Sure of a Goalie s Talent Level? Introduction Results A Few More Conclusions The Repeatability of Fantasy Hockey Stats - Part I Introduction Correlation Conceptual Arguments

4 CONTENTS Philadelphia Flyers Overview Schedule Details Player Movement Projected Lineup What to Expect Key Forward Projections Key Defensemen Projections Key Peripheral Projections Key Goalie Projections Key Team Projections Player Usage & Shot Metric Analysis Pittsburgh Penguins Schedule Details Player Movement Projected Lineup What to Expect Key Forward Projections Key Defensemen Projections Key Peripheral Projections Key Goalie Projections Key Team Projections Player Usage & Shot Metric Analysis St. Louis Blues 261

5 List of Figures 2.1 The Normal Distribution Year-to-Year PKSV% Data Shots Faced vs. PKSV% Simulation of 1000 Bad Goalies Simulation of 1000 Average Goalies Simulation of 1000 Good Goalies Scatter Plots for Two Data Sets Scatter Plots With Best Fit Lines Year-to-Year Hits Data Year-to-Year Blocked Shots Data Year-to-Year ± Data Year-to-Year Goals Data Year-to-Year PPG Data Year-to-Year GWG Data Year-to-Year SHG Data Year-to-Year Assists Data

6 CHAPTER 2. SHOOTING PERCENTAGE AS A TOOL 33 from 12% (much like the total number of heads in a coin flip experiment can deviate from 50%). But, the more and more shots a player takes, it becomes more and more likely that his shooting percentage will approach his talent level (12% in this particular example). It will be your job in your fantasy hockey draft to place your bets on players performing at or near their career shooting percentages. These are the only consistent, winning bets in fantasy hockey. Let s consider a 12% shooter in the NHL who takes 275 shots in a single season. These kind of numbers correlate well to 1st/2nd line players in the NHL, so it s important that we understand how these players behave. We can go through the typical standard deviation calculations we described in a previous section and we ll find out that the standard deviation for this type of player is right around 2%. Recalling that 68.3% of experiment results will yield values within one standard deviation of the mean, we can say that this type of NHL player will have a shooting percentage (for an entire season) between 10% and 14% in 68.3% of the experiments. What I really mean here by experiment is an NHL season (275 shots on goal). Extending this, in 95.4% of his NHL seasons, this type of player will have a shooting percentage between 8% and 16% (two standard deviations from the mean). Since most NHL careers are about years, it would be very unlikely for a player with 12% scoring talent to post a shooting percentage outside of the 8%-16% range more than once in his career. It is this concept that forms the basis for why players who post shooting percentages in one season that are wildly di erent from their career averages are expected to regress toward the mean. In our own studies (completed over a four-year period), players with high oneseason SH% typically have about a 90% chance of experiencing a strong regression. 2.4 Corey Perry s 50 Goal Season In the season, Corey Perry (who, up until that time, had never scored more than 32 goals in a season) erupted for a 50 goal outburst. Overnight, he became every fantasy hockey manager s dream pick for the draft. There was talk of 60 goals. Seriously. One fantasy hockey website claimed Perry wouldn t reach 40 goals in the season. They were roundly mocked. Let s apply our coin flip knowledge to Corey Perry. Perry s career shooting percentage (or odds that one of his shots on goal becomes a goal) is 13%. He typically takes about 252 shots in a season. If you run these numbers using our coin flip methods, you end up with about 2% for the standard deviation of his shooting percentage. So, we can make the following claims about Perry s expected shooting percentage during his career: 68% of the time, his shooting percentage should be between 11% - 15% 95% of the time, his shooting percentage should be between 9% - 17% 99% of the time, his shooting percentage should be between 7% - 19% When Perry scored 50 goals in , he was riding a 17.2% shooting percentage. Our normal distribution curve tell us that was an unlikely result. Perry should produce shooting percentages outside of the 9% - 17% range in only 5% of the NHL seasons that he plays (basically once in his career). The most likely future performance for Perry would be in the 11% - 15% range. So when our sta was putting together our projections for the season, we expected Perry to score between goals. In fact, Perry scored 37 goals in and shot with 13.4% success. This result caught many fantasy hockey managers by surprise - but it shouldn t

7 CHAPTER 3. PENALTY KILL SAVE PERCENTAGE 39 Figure 3.1: Year-to-Year PKSV% Data performance the following year. The important take-away from these plots is the following: if goalies could be good at PKSV%, you d see a lot more of them in the pink rectangles; if PKSV% were repeatable, you d see a pattern in the plotted data; because the PKSV% values are non-repeatable, they must be heavily influenced by luck. Another way to test whether or not PKSV% is repeatable is to observe goalie performance over many, many shots. If PKSV% is repeatable, then goalies who are good at it should have high PKSV% values after a lot of shots faced. And goalies who are bad at it will have low PKSV% values after a lot of shots faced. So, let s do that. Let s plot the cumulative PKSV% values of many NHL goalies vs. the number of shots they faced while on the penalty kill. We ll put the shots faced on the y-axis and the PKSV% on the x-axis. Thus, when you look at the plot in Figure 3.2, goalies very low on the chart have faced few shots and goalies very high on the chart have faced a lot of

8 CHAPTER 5. WHEN ARE WE SURE OF A GOALIE S TALENT LEVEL? 49 Figure 5.3: Simulation of 1000 Good Goalies

9 CHAPTER 7. THE REPEATABILITY OF FANTASY HOCKEY STATS - PART II 54 Figure 7.1: Year-to-Year Hits Data plots will have an R 2 value. We ve computed these values and averaged them for convenience. For the hits category, the average R 2 value is This is a great start. Remember, high R 2 values mean that the stat is repeatable. If the stat is repeatable, it is dominated by a player s skill and not by luck. And therefore, the stat can be accurately projected for future seasons. If your league uses hits as a stat, then our hit projections should be an important factor in your drafting decision. What we re saying here is that our level of certainty in our hits projection is high. 7.3 Blocked Shots Another example of a fantasy hockey stat with high repeatability is blocked shots. In Figure 7.2, we plot data for NHL players over a four season period. Again, a clear relationship is established between data from one season and the season immediately following it. The R 2 value for blocked shots has been determined to be 0.86 and is one of the strongest correlations of any stat in fantasy hockey. If you re drafting players

10 CHAPTER 11. ANALYSIS OF THE NHL SCHEDULE 71 Table 11.2: NHL Schedule Data Team Back-to-back Sets Anaheim Ducks 13 Arizona Coyotes 13 Boston Bruins 16 Bu alo Sabres 19 Calgary Flames 10 Carolina Hurricanes 16 Chicago Blackhawks 15 Colorado Avalanche 12 Columbus Blue Jackets 19 Dallas Stars 12 Detroit Red Wings 12 Edmonton Oilers 11 Florida Panthers 12 Los Angeles Kings 9 Minnesota Wild 13 Montreal Canadiens 16 Nashville Predators 11 New Jersey Devils 18 New York Islanders 16 New York Rangers 13 Ottawa Senators 13 Philadelphia Flyers 14 Pittsburgh Penguins 17 San Jose Sharks 11 St. Louis Blues 14 Tampa Bay Lightning 13 Toronto Maple Leafs 18 Vancouver Canucks 12 Washington Capitals 16 Winnipeg Jets NHL Schedule Quick Hits Enroth/Neuvirth should each get many starts as Bu alo has 19 B2Bs. If Hiller or Ramo win the starting job, there aren t many B2Bs available to the backup. Carolina s B2B total drops from over 20 last season to 16. Khudobin will see a jump from his 34 starts last season. Varlamov should command many starts this season with Colorado involved in a low 12 B2Bs. Columbus leads the league with 19 B2Bs this season. This could bite into Bobrovsky s starting load a bit (he started 58 games last season, but su ered a four-week injury in December). Lehtonen gave Dallas 64 starts with solid numbers last season. With only 12 B2Bs this season, and Lindback riding shotgun, you can expect the Stars to ride Lehtonen into the mid-60s again. Expect Luongo to get at least 65 starts given that Florida has only 12 B2Bs. One of the more interesting cases involves the Los Angeles Kings. They have a league-low nine B2Bs which surely means Jonathan Quick is primed for many starts. Montreal has 16 B2Bs this season which could lead to a drop in starts for Carey Price. A healthy Rinne could be a sure-bet in Nashville with only 11 B2Bs and a shaky backup in Hutton. The Devils B2B total drops from 20+ to 18 this season. But Schneider is the #1 this year and will get 60+ starts starts is typically the spot for Marc-Andre Fleury. With Greiss on board as backup and 17 B2Bs on the schedule, it looks like Fleury will hit his normal totals.

11 Chapter 15 Anatomy of a Yahoo Pro League 15.1 Background Yahoo Fantasy Hockey o ers users the chance to compete in Yahoo Pro Leagues for a chance to win prize money. There are two levels of Yahoo Pro Leagues distinguished only by the cost of the buy-in (and subsequent payouts). This section of the draft kit provides you with a behind the scenes look at how competitors win money in these leagues. Our emphasis will be on Rotisserie style leagues, but many of the tips in this chapter can be applied to Head-to-Head (H2H) style Pro Leagues (with the caveat that Headto-Head leagues are influenced to a greater extent by luck than Rotisserie leagues). The data presented here have been compiled over a several year period and is found exclusively in this draft kit. We hope our users will be able to apply this knowledge to dominate the Yahoo Fantasy Hockey Pro League landscape Description of Leagues The Yahoo Pro Leagues come in two flavors: PRO20 and PRO100. There is a $20.00 (USD) buy-in for the PRO20 league and a $ (USD) buy-in for the PRO100 league. You pay this entry fee (they accept PayPal) and you are entered into a league with 11 other random competitors. The only choice you have over which league you join is the draft date and time (e.g., you can choose to participate in the 8:00 PM draft on September 29). Yahoo Pro Leagues pay out money to the top three finishers in the standings at the end of the season (or the top three playo teams for H2H leagues). Below, you can find the payout data for the two league types: Table 15.1: Pro League Payouts League 1st 2nd 3rd PRO20 $ $70.00 $30.00 PRO100 $ $ $ Scoring Settings The Yahoo Pro Leagues use the default scoring settings on Yahoo. For starters, this means that every league will have 12 managers (any less, and the league is folded before the draft and your money is returned). Rotisserie leagues use the settings shown in Figure 15.1 (note: trade deadline date changes with each season), while H2H leagues use the settings shown in Figure The scoring categories are identical, but there are subtle di erences in how roster moves can be made. 92

12 CHAPTER 15. ANATOMY OF A YAHOO PRO LEAGUE 97 Figure 15.3: FSI Draft Spreadsheet

13 CHAPTER 19. LEGEND FOR DRAFT KIT SPREADSHEETS 115 Table 19.1: Legend for Skaters Spreadsheet Abbreviation Definition NHLPOS Player s position as defined by the o cial NHL website. YPOS Player s position as defined by the o cial Yahoo fantasy hockey website. ADP Yahoo Average Draft Position. AUC Yahoo Average Auction Value. OWN Yahoo Percentage of all leagues in which player was drafted. EADP ESPN Average Draft Position. EAUC ESPN Average Auction Value. EOWN ESPN Percentage of all leagues in which player was drafted. DAY1AGE Player s age on October 8, CSHPCT Career shooting percentage. AEVTOI Average even-strength time on ice. APKTOI Average penalty kill time on ice. APPTOI Average power play time on ice. ATOI Average time on ice. CG Career average goals expressed as a rate per 82 games played. CA Career average assists expressed as a rate per 82 games played. CPTS Career average points expressed as a rate per 82 games played. CSOG Career average shots on goal expressed as a rate per 82 games played. 3YGP 3YG 3YA 3YPTS 3YSOG 3YH 3YBS 3YFOW 3YSHG 3YSHA P G P A P PTS P H P BS P FOW P PIM P PPG P PPA P PPP P AT OI PR Three-year average games played expressed as a rate per 82 games played. Three-year average goals expressed as a rate per 82 games played. Three-year average assists expressed as a rate per 82 games played. Three-year average points expressed as a rate per 82 games played. Three-year average shots on goal expressed as a rate per 82 games played. Three-year average hits expressed as a rate per 82 games played. Three-year average blocked shots expressed as a rate per 82 games played. Three-year average face-o wins expressed as a rate per 82 games played. Three-year average shorthanded goals expressed as a rate per 82 games played. Three-year average shorthanded assists expressed as a rate per 82 games played. Projected goals for the season. Projected assists for the season. Projected points for the season. Projected hits for the season. Projected blocked shots for the season. Projected face-o wins for the season. Projected penalty minutes for the season. Projected power play goal for the season. Projected power play assists for the season. Projected power play points for the season. Projected average time on ice for the season. Performance Rating. An overall score assigned to assess the player s worth.

14 CHAPTER 19. LEGEND FOR DRAFT KIT SPREADSHEETS The Goalies Spreadsheet Table 19.2: Legend for Goalies Spreadsheet Abbreviation Definition ADP Yahoo Average Draft Position. AUC Yahoo Average Auction Value. OWN Yahoo Percentage of all leagues in which player was drafted. EADP ESPN Average Draft Position. EAUC ESPN Average Auction Value. EOWN ESPN Percentage of all leagues in which player was drafted. DAY1AGE Player s age on October 8, EVSVPCT even-strength save percentage. PKSVPCT penalty kill save percentage. PPSVPCT power play save percentage. CGP Career games played. CW Career wins. CGAA Career goals against average. CSVPCT Career save percentage. CSHO Career shutouts. 3YGS 3YW 3YSV 3YGAA 3YSVPCT 3YEVSVPCT 3YPKSVPCT 3YSHO 3YTOI P GS P W P L P OTL P SA P SV P GA P GAA P SVPCT P SHO Three-year games started total. Three-year wins total. Three-year saves total. Three-year goals against average. Three-year save percentage. Three-year even-strength save percentage. Three-year penalty kill save percentage. Three-year shutouts total. Three-year minutes played total. Projected starts for the season. Projected wins for the season. Projected regulation losses for the season. Projected overtime losses for the season. Projected shots against for the season. Projected saves for the season. Projected goals against for the season. Projected goals against average for the season. Projected save percentage for the season. Projected shutouts for the season.

15 CHAPTER 22. ANAHEIM DUCKS Schedule Details The Anaheim Ducks have 13 back-to-back sets this season. In Figure 22.1, we ve broken down their schedule by days of the week. Figure 22.1: Anaheim Ducks Schedule By Day

16 CHAPTER 30. COLUMBUS BLUE JACKETS 176 Figure 30.2: Columbus Blue Jackets Player Usage Chart

17 CHAPTER 40. NEW YORK ISLANDERS What to Expect Key Forward Projections Projections for Forwards Player Goals Assists Points SOG John Tavares Kyle Okposo Frans Nielsen Ryan Strome Mikhail Grabovski Josh Bailey Key Defensemen Projections Projections for Defensemen Player Goals Assists Points SOG Lubomir Visnovsky Calvin de Haan Travis Hamonic Key Peripheral Projections Peripheral Leaders Player Hits BS PIMs FOW Matt Martin 383 Travis Hamonic 169 Matt Martin 99 John Tavares Key Goalie Projections Projections for Goalies Player W GAA SV% SHO SA SV Jaroslav Halak Chad Johnson