Hmmm

Somehow all of my posts (all 4 or 5 of them) got blown away.  That’s odd.  I’ll try and reconcile it when I get home tonight

What have the injuries cost the Blues?

Clearly the Blues have suffered a non-trivial amount of injuries to their top players, and their play has suffered because of it.  The logical question is how many “theoretical wins” have the injuries cost.  If I use the crude GVT projections I posted a while back and prorate the (GVT/gm)*games missed I get the Blues to have lost ~9 theoretical goals which is a little over 1.5 wins (assumes replacements are in fact replacement level).  That said, as I mentioned at the time of the projections they did not include any aging, so likely under-estimated the Blues young core.  Unfortunately most of the missed man-games have been from that young core.  My best guess, then, is probably somewhere in the 2-3 win range.  Hopefully it won’t come back to haunt us in the end.

 

Unrelated note:  the top 4 Blues in GVT are Halak, Backes, Petro, and Brewer.  Yes that says Brewer.  He’s been worth nearly a win already.

What Does the Hot Start Mean Part 2

Last time I looked at some quick regression based analysis on how the blues hot start could impact its final season point total.  As a companion to that piece I looked at all of the post lockout teams to accrue at least 20 pts in the first 12 games.  Here is how they finished:

Team	Season	Points in 1st 12	Final Points	Div result	Conf Result
Anaheim Ducks	2007	21	110	1	T2
Buffalo Sabres	2007	23	113	1	1
Dallas Stars	2007	20	107	T2	T4
Minnesota Wild	2007	20	104	2	7
Detroit Red Wings	2006	22	124	1	1
Ottawa Senators	2008	22	94	T2	T7
Ottawa Senators	2006	20	113	1	1
San Jose Sharks	2009	20	117	1	1
Colorado Avalanche	2010	20	95	2	8
Pittsburgh Penguins	2010	20	101	2	3

The good news is none of the teams missed the playoffs. The rest is a little of a mixed bag. There were quite a few division winners and conference powerhouses, but there were also some teams that faded.

What Does the Hot Start Mean?

I’ll likely do more analysis on this subject, but here a quick way to wrap your arms around it.  Currently the Blues sit at 20 pts through 12 games.  If they were to finish the season at the pt/gm pace of the past two seasons (which is to say a non-playoff team) of 1.1 pts/gm then they would finish with 97 pts, likely good for a playoff spot.  How much better are they than a 1.1 pt/gm team is the real question?  If I weight the 1.1 pts/game at 100 gms and the 1.67 pt/gm displayed so far this year at 12 games then the weighted average is ~1.16 pt/game.  Applying that to the remaining 70 games would yield ~101 pts.

Quick Stat

Sorry I haven’t kept up with this project; hopefully real life will settle down enough to get back into this.  That said, here’s a quick stat I found interesting:

 

Halak notched shutout #3 the other night.  Last year it took 46 games for the note to have 3 shutouts.

Player Aging Part 1

This will be the first post of a yet to be determined number of posts as I investigate aging curves for various stats.  Gabe over at Behind the Net has already done quite a bit of work on this topic, so some of this will be a rehashing of things he’s already investigated.  Our methodology differs slightly (honestly his is likely better) so the results, while similar, won’t be the same.  For the first pass I chose to use the delta method with no correction for survival bias.  What does this mean?  I took all players that played both an age 18 and 19 season and calculated their increase/decrease in the various stats.  I then calculated the same for all age pairs.  Once that’s done I found a weighted average of the increases/decreases for each age pair where the weight is equal to the minimum number of games played between the two ages.

The first stat I chose to look at is pts/game.  My input data is 2001-2010.  The following table summarizes the results by general position

Age	All	F	D
18_19	0.140	0.160	-0.108
19_20	0.068	0.072	0.052
20_21	0.026	0.026	0.023
21_22	0.049	0.050	0.045
22_23	0.023	0.025	0.018
23_24	0.010	0.022	-0.018
24_25	0.002	-0.009	0.024
25_26	0.020	0.027	0.007
26_27	-0.022	-0.021	-0.023
27_28	-0.027	-0.039	-0.003
28_29	-0.024	-0.022	-0.028
29_30	-0.012	-0.030	0.017
30_31	-0.038	-0.044	-0.028
31_32	-0.052	-0.069	-0.022
32_33	-0.045	-0.057	-0.021
33_34	-0.057	-0.062	-0.048
34_35	-0.067	-0.079	-0.046
35_36	-0.080	-0.083	-0.071
36_37	-0.058	-0.074	-0.022
37_38	-0.043	-0.042	-0.044
38_39	-0.089	-0.128	-0.002
39_40	-0.070	-0.073	-0.063

The table shows the age pair in the first column and then the respective increase/decrease in the other three columns.  These numbers seem to indicate a peak around 26.  The following graph shows the same data in graphical form

I also ran a similar analysis on average time on ice (ATOI).  Here’s the table

	ATOI
18_19	2.62
19_20	0.93
20_21	0.90
21_22	1.18
22_23	0.82
23_24	0.59
24_25	0.57
25_26	0.32
26_27	0.03
27_28	0.05
28_29	0.03
29_30	-0.10
30_31	-0.30
31_32	-0.54
32_33	-0.48
33_34	-0.78
34_35	-0.70
35_36	-1.13
36_37	-0.97
37_38	-0.83
38_39	-0.96
39_40	-1.16

and the graph

Here the positional difference was smaller (although I may graph it in a late post). Overall there appears to be a fairly flat peak between 26-30.

Next up I’d like to investigate aging of some of the more advanced stats (i.e. GVT, relative +/-, etc.)

The Blues and GVT

For those familiar with baseball analysis they are familiar with the concepts of Runs Above Average and Wins Above Replacement in which players are compared to either average players or the “replacement player”. The replacement player is the level of production expected of the guys a team could bring up from the minor leagues to fill out a roster. For more read here. One of my initial questions of Google when I first started looking into advanced hockey stats was whether anyone in hockey was tracking/publishing similar statistics. In my searches I found Tom Awad’s GVT. Read here for an explanation. GVT is expressed in goals. The following table shows how the Blues players have fared over the last 3 years

Player	07_08	08_09	09_10
Alex Pietrangelo		-0.1	-0.9
Alexander Steen	6.8	2.7	10.5
Andy McDonald	6.3	6.8	10.4
B.J. Crombeen	-0.1	2.1	1.2
Barret Jackman	3.7	5.1	6
Brad Boyes	15.8	13.9	6
Carlo Colaiacovo	1.1	7.1	9
David Backes	2.1	12.3	8.5
David Perron	5	11.3	6.2
Eric Brewer	4.1	-0.1	2.6
Erik Johnson	7.4		10.2
Jaroslav Halak	3.5	9	20.9
Jay McClement	1.2	5.4	6.3
Jonas Junland		-0.1	-0.1
Patrik Berglund		11.4	1.6
Roman Polak	0.4	2.1	7.3
T.J. Oshie		10.2	10.6
Tyson Strachan		2.1	1

For those that want to convert to goals, the conversion I’ve seen is 5.6 goals/win. The chart shows pretty well that Brewer is in fact fairly useless; nearly replacement level in fact. Also sticking out to me are Oshie and Johnson’s solid numbers (fairly expected) and Berglunds plummet (less so). All that said, what can say about the future based on these numbers? To answer that I ran a quick 3-2-1 projection.

	GVT
Alex Pietrangelo	-0.6
Alexander Steen	7.0
Andy McDonald	8.8
B.J. Crombeen	1.5
Barret Jackman	5.3
Brad Boyes	10.3
Carlo Colaiacovo	7.7
David Backes	8.8
David Perron	7.8
Eric Brewer	2.5
Erik Johnson	9.6
Jaroslav Halak	16.7
Jay McClement	5.2
Jonas Junland	-0.1
Patrik Berglund	5.7
Roman Polak	5.3
T.J. Oshie	10.5
Tyson Strachan	1.8

This projection does not apply aging and that could be important for the Blues with some guys still on the upward swing of the aging curve. Also of note, GVT is a counting stat, so playing time (and injuries) would impact the previous projections. In an attempt to get around that I ran a new set where I projected a GVT/game played and then prorated to a “full season” (76 Games for skaters and 60 for Halak)

Player	GVT	WAR
Alex Pietrangelo	-5.1	-0.9
Alexander Steen	7.5	1.3
Andy McDonald	9.4	1.7
B.J. Crombeen	1.4	0.3
Barret Jackman	5.5	1.0
Brad Boyes	9.5	1.7
Carlo Colaiacovo	8.6	1.5
David Backes	8.4	1.5
David Perron	7.5	1.3
Eric Brewer	2.9	0.5
Erik Johnson	9.4	1.7
Jaroslav Halak	25.2	4.5
Jay McClement	4.8	0.9
Jonas Junland	-3.5	-0.6
Patrik Berglund	5.7	1.0
Roman Polak	5.3	1.0
T.J. Oshie	11.6	2.1
Tyson Strachan	6.5	1.2

That would be a stellar year from Halak and a pretty good year from TJ. The rest is kinda just eh. Not too bad, but not too good. Thankfully the SD around those projections is probably pretty high for Bergy, Perron, and Johnson. Any could probably jump into the 2.5+ WAR category. All said, that team would probably be about the same as last year, somewhere in the low 90s in points if they met those projections.

Let’s Get This Started

Hello everybody and welcome to my new side project, Stl Blues By the Numbers. Around these parts I hope to explore the statistical analysis side of hockey much the same way I have the analysis side of baseball. My focus will be on the St. Louis Blues, but I’d guess that I’ll delve into NHL wide topics from time to time. I’ll say upfront that I have very little background in advanced hockey stats, so there will definitely be a learning curve that we can all experience together.

For those that are unfamiliar with my work on the baseball side of analysis I’ve spent the last year or so writing about the Cardinals at Play a Hard Nine and general MLB at Beyond the Boxscore and Fangraphs (infrequently for both of those). For those that are familiar with my work there, don’t worry I’ll still be spending plenty of time doing baseball analysis. Like I said earlier this blog is a side project, but I hope that it will fill a small niche in the blogosphere. We’ll see.

Enough introductions, let’s actually throw some content your way. We’re going to start simple. The following table is simply a first pass at projecting player point totals for next year. It simply uses a weighted average of the last 3 seasons of data (if available) using a 3-2-1 weighting scheme. Right now there is no aging or regressing to the mean applied. That step will come in a later post once I do a little more research into those topics. The following are based on more or less full seasons of playing time (think upper 70s in GPs)

Player	G	A
Andy McDonald	23	39
T.J. Oshie	18	30
David Backes	21	26
Alex Steen	17	23
David Perron	18	28
Brad Boyes	25	31
Erik Johnson	9	29
Carlo Colaiacovo	5	26
Jay McClement	11	16
Patrik Berglund	16	18
Roman Polak	3	16
Barret Jackman	3	16
Eric Brewer	6	11

Next time I’ll do a similar calculation on Tom Awad’s GVT and see how that falls out.