Py Rankings Week 14

Calgary finally jumps into first place - sort of.  Four straight losses aren't quite enough to knock Saskatchewan from the top of the heap, but they do fall into a tie for first with Calgary.  After spending most of the season as the highest scoring and strongest defense, the Riders drop into a tie for second for the scoring lead, while remaining the top scoring defense.

A strong showing by the Argos wasn't enough to get them any math love, as a dominant BC win bumps them up to 3rd place.

Edmonton continues to underperform, the math gods still don't like Montreal, and then there's Winnipeg.

Luckiest Team: Tie - Calgary and Toronto (+1.5 wins)
Unluckiest Team: Edmonton (-2.6 wins)

Biggest Jump: BC (+1.0 projected wins)
Biggest Drop: Hamilton (-0.7 projected wins)

Does winning close games help in the playoffs?

A while back, I added the "Big Win" stat to the Py Win Rankings.  The idea behind this stat is that it counters the notion that "good teams win close games", and that winning close games prepares a team for success in the playoffs.

The idea for the stat, and the logic behind it, comes from Jim Glass of advancednflstats.com.  In his article, he analyzes playoff records for the best playoff teams, and groups them by their record in close games and blowouts.  I'm following his example with the CFL data.

Test Data

Like Mr. Glass, I will filter the data to the top tier playoff teams, those which made the playoffs with at least 10 wins..  More wins means of course that they will have more "big wins" or "close wins" respectively.  It will filter out some Grey Champions which didn't finish in the top of the league, but the goal here is simply to determine if big wins or close wins are more closely related to playoff success, not analyze each Grey Cup winner in detail.

Filtering to 10+ win teams since 1990 leaves us with 93 teams.

I'm using 9 points or more to represent the cut off between blowout and close win.  The original NFL article used 10 points, but he later amended it to 9 points, which represents the cutoff between a 1 and 2 possession game.

Results

Most close wins:

• Of the 93 teams, 8 won 8 or more close games.  Their record in the playoffs was 5-7.
• The 15 teams with the best records in close games combined to be 91-14 (87%) in those close games.  In the post-season, they had a record of 17-11 (60%), 6 Grey Cup appearances, and 4 winners.

 Fewest close wins:

• 20 of the 93 teams has losing records in close games.  The 15 with the worst record in close games combined for a record of 34-66 (34%).  In the post season, they had a record of 19-11 (63%), 9 Grey Cup appearances, and also 4 winners.

It doesn't appear that a team's record in close games matters very much at all, as both the best and worst teams in close games have very similar records come playoff time.

 Grey Cup Winners:

Looking at things from the perspective of the 20 champions (the 2012 Argos, 2000 Lions and 2001 Stamps didn't make the 10 win cut), they're win-loss records shake out as follows:

• The playoffs: 47-0 (of course)
• The regular season: 252-106-2 (70%)
• Close games during the regular season: 81-52 (61%)
• Big wins/losses during the regular season: 171-54 (76%)

A 61% win rate in close games does show some ability to win close games, though not much better than a coin flip.  They were slightly better than the rest of the 10 win teams, however, as the average for all 93 teams was 60%.

Playoff results by win cohort:

Here's how winning close games matches up with winning playoff games, grouped by W-L record.  For the purposes of this exercise, I'm considering a tie to be a loss (it's not "clutch" to tie, right?).  Only 2 teams finished with ties.  (Value in brackets is winning percentage in close games).

15-3 (7 teams)
Top 3 (83%): 6-1, 2 GC winners
Low 4 (68%): 7-2, 2 GC winners

14-4 (4 teams)
Top 2 (86%): 1-2
Low 2 (69%): 1-2

13-5 (17 teams)
Top 8 (82%): 8-6, 2 GC winners
Low 9 (51%): 10-5, 4 GC winners

12-6 (20 teams)
Top 10 (72%): 9-1, 3 GC winners
Low 10 (49%): 15-8, 2 GC winners


11-7 (24 teams)
Top 12 (69%): 11-10, 2 GC winners
Low 12 (46%): 11-10, 2 GC winners

10-8 (21 teams):
Top 10 (61%): 10-9, 1 GC winner
Low 11 (40%): 9-11

The two groups win at nearly the same rate.

• The "higher halves" have a much better record in close games, but a 45-29 record in the playoffs (61%).
• The "lower halves" are much worse in close games, but have a similar record at 53-38 (58%), and more Grey Cup winners (10 vs 9).

The data isn't as cut and dry in the CFL as it is in the NFL (where the lower half of each group clearly has a better winning percentage), but the numbers are extremely close.  Close enough to suggest that a team's record in close games may not have much to do with playoff success, either positively or negatively.

Big wins and big losses:

Perhaps then, big wins and losses are a better indicator of playoff success than close wins?

• 5 teams had 11 or more big wins in a season.  Their playoff record was  6-3 (67%), with 2 Grey Cup wins.
• 15 teams had 10 or more big wins.  Their record in the playoffs was 22-8 (73%) with 7 Grey Cup wins and 3 Grey Cup losses, meaning 10 of those 15 teams made the Grey Cup.

Conclusion

It seems clear that close wins do not equal playoff success.  Of those 8 teams with 8 or more close wins, only the '95 Stallions had a successful play off run - they went 3-0 and won the Grey Cup.  The remaining 7 only appeared in 1 Grey Cup, with no wins.

However, while "big" wins do appear to correlate better with Grey Cup wins than close wins, they don't appear to correlate any better than straight up wins and losses.  I think I will explore this in better detail in a later post, but I believe this comes down to the CFL having less scheduling variance than the NFL.

For now, I will continue to include the "big win" stat on my rankings table, as I think it is interesting, but I suspect that more analysis will show that it simply lines up quite closely with overall win-loss records, and doesn't give us much useful information.  In fact, it may be more beneficial to include "close wins" instead, as an indicator of teams which may do poorly in the playoffs, vs using big wins to indicate those which will do well.

Py Rankings Week 13

The Riders keep losing, the Argos keep winning, and the rankings stay the same for the second consecutive week.

Saskatchewan, despite a 3rd straight loss, cling to a small lead in the stats, continuing to be the highest scoring offense and stingiest defense.

Luckiest Team: Tie - Calgary and BC (+1.5 wins)
Unluckiest Team: Edmonton (-2.2 wins)

Biggest Jump: Edmonton (+0.4 projected wins)
Biggest Drop: Calgary (-0.4 projected wins)

Py Rankings Week 12

The Riders lose another, but Py Expectation still thinks they are the best team in the league, 0.4 wins ahead of Calgary.  Edmonton moves up 2 spots to sixth (take heart, Eskimo fans, the numbers suggest you could be looking at 7-8 win season by the time we're done).

Luckiest Team: Calgary (+1.9 wins)
Unluckiest Team: Edmonton (-2.5 wins)

Pythagorean Stats since 1990

Pythagorean (py) wins (what are py wins?), and Big Wins are interesting stats, but they don't tell us much on their own.  In order to put perspective on them, it's necessary to look at historical data, and see if they have useful, or even any, connection with past seasons.

Py Wins As a Prediction Model

The idea behind the pythagorean expectation formula is that points for and against provide a better indication of team quality than actual wins and losses, and that over time, teams which significantly over or underperform their expectation tend to regress or improve back to expectations.  NFL and MLB statisticians use historical data to provide perspective on what kind of regression or improvement a team a team is likely to show in the next season, or even half season.  I now have data dating back to the 1990 season, which I can use to gather the same data (in the future I will look at pre-1990 seasons, but I expect that as you go back in time, the changes to the game will start to hurt the accuracy of our current data):

   

Over the past 182 seasons (that's 1 season per team since 1990, including Ottawa twice and the failed American teams), you can see how many teams finished above or below expectation, and how they did in the following season.  Seasons where the team was not in the league the following year have been removed from the table and chart.  The 2012 and 2013 seasons are also not yet included, as they have no follow up season to analyse.

As you can see, the majority of seasons fall into a range quite near to expectation: 41 of 182 fall between -0.5 and 0.5, and 100 between -1 and 1.  That's pretty good; 55% of teams finish within 1 win of expectation, and less than only 28 times in 31 years has a team missed expectations by more +/- 2 wins. 

In the ranges where we have more data, the chart follows the line you would expect; teams which miss expectations tend to turn it around the following year, while teams which surpass them end up with a few less wins the next year.

There are of course some outliers in the data at the outer edges where we have poor sample sizes.  In 1997, Montreal finished a full 4.5 wins above expectation, winning 13 games despite a -23 point differential.  Defying the expectations, they won another 12 games in 1998, finishing another 2.7 wins up on expectations.  On the other end of the spectrum, we have the 2010 Blue Bombers, finishing 4.5 wins below expectation.  They had extraordinarily bad luck that year, winning only 4 games despite a point differential better (-21) than those '97 Als.  The next year, Winnipeg won 10 games and made it to the Grey Cup.

Neither one of these examples gives us a good idea what to expect when a team is so far above or below expectation, simply because it's so uncommon.  Were the Bombers lucky to turn it around?  Were the Als lucky to avoid regression?  I think the latter is likely the case based on the ranges where we do have more data, but no one can say for sure.

All in all, I'm comfortable with saying now that as with other sports, Pythagorean Expectation is a good way to predict future performance in the CFL.

Advanced CFL Stats - Week 11

The week is over, so it's time for more stats.

This week the Riders got a little less lucky, the Bombers got a win in the new stadium, and the Eskimos just can't buy a break.


I stream-lined the chart a bit this week and it's presented in a slightly different format, as my stats are now in a database instead of a spreadsheet, so I can store more and do cooler things, like:

Big Win Percentage.

Big Win Percentage is a simple stat, created by Jim Glass. It's based on the premise that football by nature is a game that can be heavily influenced by luck. A bad call, a fumble recovery, a gust of win; these are all things which can turn a close game into a win or a loss. According to Brian Burke (the guru of NFL stats), the outcome of more than 40% of NFL games is determined by random chance. This makes judging a team by it's record a difficult proposition (especially in the NFL, where teams don't play every team in the league).

What Mr. Glass's formula does it try to account for that luck by giving teams credit for "Big Wins", defined as a game decided by 9 or more points. 9 points makes a good cut off because it is the border between 1 and 2 possession games.

The formula is simple - games won by 9+ points count as a "Big Win", games lost by 9+ points are considered a "Big Loss", and all the rest are considered ties. If you read the article linked above, you'll see that he's found that teams with a high number of "Big Wins" in a season tend to fare much better in the playoffs. We'll see if that holds true for the CFL (I'm compiling data back to 1990 for a post later this week), but in the mean time, I'm going to include it on the chart for this week.
 

Py W = Pythagorean Wins, Projected = Py Wins over 18 games

The Riders remain the best team in the league based on Py Expectation, but they are no longer considered the luckiest team in the league, that honour now goes to Calgary.  Edmonton remains the unluckiest team so far, nearly 3 wins below expectation.  Winnipeg, despite a win over the Riders this week, still sits at the bottom, though they are still considered unlucky by the formula.

Coming soon...

As noted above, I've been collecting data, back to 1990 so far.  I plan to do a post to highlight some of the interesting points once I have a bit more information gathered.

- Mike

CFL Pythagorean Wins

I'm a big believer in statistics and analysis when it comes to sports.  As noted by some on /r/cfl previously, there is a significant lack of advanced stats for the CFL.  I'm not a statistician, nor do I have charting stats for each any every game like the NFL stats sites, so there are definite limits on what I can provide, but one stat I can calculate easily is Pythagorean Wins.

Bill James created the formula for baseball years ago, and it's been modified to better suit the NFL since then.  Obviously the CFL is not the NFL, but the season is of similar length and scoring numbers are also in the same ball park, so I believe the stat should apply fairly well to our league.  Down the line I will look at some past seasons and see if I can determine how well (or poorly) it actually does work.

The formula itself is based on the idea that not all wins are created equal, and that point differential is actually a better indicator of future winning percentage than actual wins and losses.  When applied to NFL games, the stat is a good indicator of future performance, both for future seasons, and second halves of the same season.

For a more detailed explanation from someone much smarter than I, check out Bill Barnwell's explanation on grantland.com.

With all of that said, we are at the half way point of the CFL season, so this is a perfect time to run the numbers on the first half and see what they might tell us.

Legend
P-W%: Pythagorean Winning Percentage, P-W: Pythagorean Wins, P W-L: Pythagorean Win-Loss,
Diff: Difference between Py Wins and Actual wins, P-W-T: Pythagorean Win Total (projected over 18 games)

By the numbers, Saskatchewan and BC are the luckiest teams of the first half, while Edmonton and Winnipeg are the unluckiest.  Despite being the luckiest team, the formula still believes that the Riders are the best team in the league, while Edmonton has been particularly unlucky, performing almost 2.5 wins below expectation. 

Teams which over or under perform the formula by a wide margin tend to fall back or climb closer to their expected win total as the season progresses, so according to Pythagoras, both Edmonton and Winnipeg fans should have some hope that their team will rebound slightly in the second half.  That said, there aren't many surprises here, other than some shuffling in the middle.  The formula believes that Toronto is slightly better than BC (but clearly isn't aware that Ricky Ray is injured), and that Montreal is slightly worse than Hamilton.