Category: Hockey

A Historical Review of ESPN Power Rankings

By Ben Ulene

It’s September again, and with the major wintertime sports starting their 2016-2017 regular seasons, sports fans across the U.S. get to participate in the annual tradition of poring through expert predictions – including various outlets’ preseason power rankings.

While Vegas odds and other betting markets offer a general take on how various teams may stack up, there is something satisfying about reading power rankings reports. With written blurbs for teams that function as justifications for their rankings, sites like ESPN and Bleacher Report impart a qualitative aspect to the numbers that may mirror, or perhaps even spark, debates among fans across the country. And like professional odds-makers, many sporting news outlets update rankings as the season goes along – bookending the year with a final set of rankings leading into the playoffs – which provides a mechanism for determining how “predictable” any given regular season is.

For ease of use, I analyze ESPN’s Power Rankings in this article. With rankings from the first to last week of the regular season across all four major U.S. sports, ESPN’s rankings let us perform cross-sport comparisons to test the accuracy of predictions by different sets of “experts,” as well as dive deeper into individual sports to determine the predictability of different teams and seasons.

 

Cross-Sport Analysis:

Upon analyzing the past six years of rankings across MLB, NBA, NFL, and NHL, one thing is apparent: The NBA is consistently the most predictable league, and by far. As a few scatterplots show, in the NBA, Week 1 Power Rankings are much more predictive of regular season success than in any other sport:

Here, a straight diagonal line would represent perfect prediction accuracy for every team; while the NBA plot is far from perfect, it still seem to be noticeably less random than the other plots.

In fact, as a two-sided t-test shows[1], the inter-league difference in predictability (measured by the average absolute value difference for every team’s beginning ranking and their final ranking) is statistically significant between the NBA and every other league, but insignificant among the other three. In other words, the predictabilities of MLB, NFL, and NHL regular seasons cannot be proven to be different from one another – but the NBA is more predictable than all three:

League 1 Mean Difference League 2 Mean Difference p-value
NBA 4.33 MLB 6.94 1.151e-07
NBA 4.33 NHL 6.54 6.178e-06
NBA 4.33 NFL 7.52 3.242e-09
MLB 6.94 NFL 7.52 0.3327
MLB 6.94 NHL 6.54 0.4735
NFL 7.52 NHL 6.54 0.101

 

Why then – besides the unlikely explanation that ESPN’s NBA analysts are that much better than their analysts for other sports – is there such a drastic difference in ranking accuracy? Season length is what first comes to mind, but upon closer inspection cannot be the primary cause; not only is the NBA season easier to predict than the equally-long NHL season, but there is a noticeable lack of statistical difference between predicting the MLB (162 games) and NFL (16 games) seasons.

There are, however, a few possible explanations:

1) Fewer, more impactful players: The NBA mandates that teams carry 14 players at any given time, as opposed to the NHL’s 20, MLB’s 25, and the NFL’s 53, giving star players – who are usually apparent at the beginning of the season – more of an impact on results.

This is magnified by the nature of the game: Only in the NBA do the most impactful players consistently play for more than three quarters of the game, making it easier for talented teams to rise to the top. Pitchers in baseball can play only every five games; hockey superstars may see only 20 minutes on the ice in a game. Even in the NFL, elite quarterbacks are only on the field for offensive snaps, and the comparatively high injury rate makes it difficult to predict even the best teams.

2) Higherscoring games: The high number of possessions in NBA games, compared to the other three sports, may mean that there is less of a risk that any given NBA game is determined by chance. NBA teams possess the ball over 90 times on average in any given game[1]; even in baseball, teams will rarely send up more than 40 batters in a game[2]. Therefore, skill differences have more opportunities to manifest themselves in basketball, while mistakes can have a larger impact on the other big sports with fewer possessions.

3) Injuries: Since hockey and football are high-contact sports, teams in the NHL and NFL are much more susceptible to being gutted by injuries midseason than NBA teams. Even in baseball, a non-contact sport, pitchers – perhaps the most important players on their teams – are highly susceptible to season-ending arm injuries that can tank a team’s season.

 

Seasonal Analysis:

Taking the league-based differences into account, it is no surprise that six of the seven most predictable seasons in our sample are in the NBA – with the 2013 NBA season clocking in with a tiny 3.47 average difference between beginning and end rankings.

seasons

Additionally, the NBA is unique in its consistency – while other sports like the NHL vary wildly from season to season in predictability, the NBA has stayed relatively constant. The NBA has been the most predictable for each of the past six years, while no other league has repeated as least predictable – the NFL led in 2010 and 2013, MLB in 2011 and 2015, and NHL in 2012 and 2014.

seasons2.png

 

Team Analysis:

Just as interesting are the numbers for different teams across sports. Not surprisingly, the Miami Heat was the most predictable team in the country over the past six years – buoyed by four years of their LeBron-powered “Big Three.” But the top of the list is not just dominated by consistently good teams – bottom-dwellers like the Philadelphia 76ers, the Edmonton Oilers, and the Houston Astros (bad until 2015) also lead in predictability. Perennially unpredictable teams like the Minnesota Vikings and Boston Red Sox dominate the bottom of the list, as well:

teams.png

To conclude, what can this data tell us about predictability in American sports as a whole? For a large majority of teams across the big four U.S. sports, not a ton – moving four spots up or down in rankings can be the difference between making the playoffs and missing out. And assuming the randomness in professional sports doesn’t change anytime soon, it is probably safe to say that expert accuracy will continue along this trend for years to come.

[1] I compared the absolute value of first week–last week differences, with n=180 for every league except the NFL, which had n=192 (since the NFL has 32 teams).

[2] http://www.basketball-reference.com/leagues/NBA_stats.html

[3] https://www.teamrankings.com/mlb/stat/at-bats-per-game

 

3v3 Overtime is Working

By Antonio Papa

This season, the NHL has initiated a rule change to create more overtime goals and fewer shootouts. Now, overtime play will be 3-on-3, instead of 4-on-4. A quick statistical analysis shows us that the new rule has – and will continue to – increase overtime scoring.

Shootouts were added after the 2005-06 lockout as an alternative to ties in the regular season, but they have been criticized as essentially flipping a coin to decide the winner. 3-on-3 play, in contrast, gives stronger teams an increased chance of scoring goals. In the early 1980s, the Edmonton Oilers even told their defenders to get into mutual roughing penalties on purpose so that the game would become 4-on-4 or 3-on-3. Then, Wayne Gretzky, Jari Kurri and Mark Messier would take over on the open ice. This was effective because players with superior skating ability gain an upper hand in 4-on-4 and 3-on-3 situations, resulting in more goals scored.

The NHL instituted the “Gretzky rule” in 1985 as a direct response to these shenanigans. The “Gretzky Rule” created the concept of coincidental minor penalties and allowed full strength play for offsetting penalties. A few years later, the NHL reversed the change in an attempt to reclaim some of that high-scoring open play. Expect this year’s 3-on-3 overtime to benefit top-heavy teams, like the Pittsburgh Penguins, who are sure to take advantage of the situation with skaters like Sidney Crosby, Evgeni Malkin and Phil Kessel.

Over the past eight seasons, 43% of overtime games had a goal and the other 57% needed a shootout (2,227 games). In this preseason’s overtime games with the rule change in place, 72% of overtime games had a goal and only 28% needed a shootout (24 games). Even with the small sample size, we can use a T-test (difference of means) to determine whether this change is statistically significant. The standard binomial error is σ = .0105 for the regular season set and σ = .0926 for the preseason set. The result is that we are 99% confident that the new rule decreases the proportion of shootouts in overtime by 40%-58% (about half) and should lead to high-octane teams winning more games in overtime.

[Editor’s Note: The last paragraph was edited post-publication to clarify the statistical test used]

Shootout for the Ages

By Antonio Papa

How unlikely was the twenty round shootout between the Capitals and the Panthers?

Last Tuesday, the Florida Panthers defeated the Washington Capitals in the longest NHL shootout ever. It was a grueling, twenty-round battle that dwarfed the previous record. The previous record for longest shootout, which only lasted fifteen rounds, took place in 2005 between the New York Rangers and the Capitals.

Just how incredible was the shootout marathon between Washington and Florida?

Shootouts themselves aren’t terribly rare; there have been 1409 since the league instituted them for the 2005-06 season. Between the 2005-06 and 2013-14 seasons 13.3% of games have been decided by shootouts. The majority of those shootouts have been resolved in the first three rounds. If the score is tied after the first three rounds then more rounds are added until one team scores and the other misses.

Here is a table that counts the number of shootouts that reached up to fifteen rounds (the previous record). The number of shootouts drops rapidly as the number of rounds increases, and barely any last more than eight rounds. Again, this data includes all games between the 2005-06 and 2013-14 (previous) season and thus does not include this season’s data.

Pic1

*Note: A shootout can end after two rounds if one team leads by 2-0 at the end of the second round (leaving no room for a comeback and resulting in an automatic shootout win).

This relationship of the number of shootouts that reach a particular round can be better described graphically. The following graph only shows shootouts that went to at least the fourth round, because the shootout rules change to sudden death after the third round. The y-axis is on a logarithmic scale.

Pic2
A function of natural log fits the plot quite well. Based on the trend line, the probability that any particular game will end in a shootout lasting twenty or more rounds is 0.00112%. If we extend this relationship, we predict that, with 1230 games in a season, we can expect a shootout like the one on between Florida and Washington to take place about once every seventy-two seasons.

Pic3

So, how rare was Tuesday’s shootout marathon?

Once in a lifetime.

There is No Place Like Home

By Jeffrey Gleason

Nine weeks into the NFL season, no teams remain unbeaten. This could’ve actually been said after eight weeks, after seven weeks, and after six weeks as well. Week 5 was the last time an unbeaten team remained, when both the Cardinals and Bengals were sitting at 3-0.

However, after these same nine weeks, five teams remain unbeaten at home. The Patriots, Broncos, Eagles, Packers, and Cardinals have yet to lose on their own turf.

Home field advantage is a phenomenon that gets a lot of traction in sports. Experts often use it to justify their predictions and betting lines usually reflect the perceived advantage of the home side. However, people often generalize home field advantage with a “one size fits all” approach, acknowledging its presence, but assuming it displays a constant impact across different situations.

With five unbeaten NFL home teams and the recent impetus of a road team finally winning Game 7 of the World Series (the Giants topped the Royals on October 29th to capture their third championship in five years), I was interested in how home field advantage was quantitatively different in different situations. How does it vary across sports? Do both good teams and bad teams experience the same advantage? Is it magnified in the postseason? What about differences in earlier eras? These are the questions I set out to resolve.

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