Olympic Men’s Ice Hockey Predictions

12 Feb

Here goes my Olympic Men’s Ice Hockey predictions.  Shortly the tournament kicks off with Czech Republic versus Sweden.  While you can take a look at the rosters and make some good determinations based on skill sets, my experience watching European hockey up close over the past two years indicates yo me that there are some other items to consider.  Most importantly, the Olympic ice size is much larger than the surface used in the NHL.  The travel time will be rough on players living in North America.

The jet lag will be bad for all NHL players…for instance, I am not sure that Ovechkin has an advantage because he is Russian by birth as he spends most his time in the U.S. and Canada during the season.

Wayne Gretzky believes the hottest goaltender and the best skater give you the best chance for winning.  Goaltending is huge, so I will agree with him there.  However, I think the best ‘set of skaters’ is much more important than the having the best individual player.  Matchups are also a big deal and the groups are already set.  Based on the Group Round, lets take a look at my predictions.

Group Round

Group A

  •  Russia
  •  Slovakia
  •  United States
  •  Slovenia

Russia is playing at home and were good in the group round for the 2013 World Championships.  With home advantage and being more used to the bigger ice, I think they have the advantage to win the group over the U.S.

The U.S. could surprise Russia on February 15th.  I predict Russia will win, so the U.S. should take second in the group.  Slovakia is a bit of a threat, but it would be an upset if they beat the U.S. or Russia here.  Slovenia probably does not have much of a chance getting off of the bottom here.

  1. Russia
  2. United States
  3. Slovakia
  4. Slovenia

Group B

  •  Finland
  •  Canada
  •  Norway
  •  Austria

Finland or Canada could take this group, but the edge has to go to Finland based on goaltending and because they have fewer NHL players on their roster (yes I said fewer).  Most players in the KHL or SM-Liiga are more than good enough to play at the NHL level, but are just better on the open ice or not as physical as necessary for the NHL.  The Finnish side has several players playing in Russia and Finland currently, which also makse jet lag a non-issue.  Whoever is second place in this division should still score a lot of goals, so they will likely have the best second place team after the group stage–giving them direct entry into the quarterfinals.

Between Norway and Austria, I think it is a bit of a toss up.  Many of the Austrian players play together in the EBEL Austrian League, where the Norwegian players are a little more spread out.  I will give the third place edge to Austria just based on chemistry.

  1. Finland
  2. Canada
  3. Austria
  4. Norway

Group C

  •  Czech Republic
  •  Sweden
  •   Switzerland
  •  Latvia

Switzerland has been the surprise team lately and played extremely well in the 2013 World Championships.  However, they will not have the firepower to keep up with what Sweden has brought to Sochi.  They should still have the edge on the Czech Republic and Latvia though, placing the Swiss side in second place.  Czech Republic can get a shot at the second spot, but are not quite as good as Switzerland.  Latvia is the obvious bottom team here and might be the worse in the Olympics.

  1. Sweden
  2. Switzerland
  3. Czech Republic
  4. Latvia

Qualification Playoff

Based on my predictions, these are the matchups for the qualification playoffs:

United States (5D) vs. Latvia (12D)

This will be an easy match for the U.S. and they will move on to the next round.

Switzerland (6D) vs. Slovenia (11D)

Another easy match with Switzerland walking all over Slovenia.

Czech Republic (7D) vs. Norway (10D)

This could be a good match, but the Czech side should be able to grind out a win in a low scoring affair.

Slovakia (8D) vs. Austria (9D)

This will be an even match, but I think Austria actually has a chance here.  Whoever wins will lose in the next round to the tournament’s top seeded club, so I will take Austria on the mini-upset here….there has to be an upset, right?

Quarterfinals

Russia (1D) vs. Austria (9D)

Austria probably does not have much of a chance here.  Slovakia either.  Their medal chances end with a Russian victory.

Canada (4D) vs. United States (5D)

This will no doubt be a great matchup and is really too close to call.  The U.S. has better speed and goaltending and in the end I think that will be more important than having Crosby (best skater under the Gretzky rule).  The U.S. will take this one and send Canada reeling without a medal.

Sweden (3D) vs. Switzerland (6D)

A rematch of the 2013 Men’s World Championship will end with Sweden victorious again.  They are just too good for upstart Switzerland.

Finland (3D) vs. Czech Republic (7D)

The Czech Republic just simply does not have a championship side and Finland will come out of this victorious.

Semifinals

Russia (1D) vs. United States (5D)

A rematch of the semifinals from the 1980 Olympics in Lake Placid and the 2013 World Championship quarterfinals.  Much less of a miracle needed this time.  Russia has home-ice advantage, but they do not have the intangibles to win championships.  They will fall again to the U.S. and will have a chance at third place.

Sweden (2D) vs. Finland (3D)

This will be the match of the tournament and should be great to watch.  For me, it is a coin flip, but edge to Finland for goaltending.

Finals

Finland vs. United States

The game will be close, but not super competitive I imagine.  Finland wins the gold and the American squad takes a silver after a hard run past Canada and Russia.

Sweden vs. Russia – Bronze Medal Game

Russia cannot be shutout in their own country, right?  Sweden can do it, but the Russian side will come out to play here.  Sweden goes from number one last year to number four and Russia celebrates third place in Sochi.

Hockey Simulator Provides another Fun Way to Look at Leagues

19 Jan

Working on the KHL and in the middle of the playoff race, I was interested in trying to predict the potential final tournament before the season is over.  Though most teams only have eight to ten games left, the long break because of Sochi means we will not know the results of a lot of leagues until March and beyond.

I considered trying to run the mathematical simulation on my own, but in my research I found a website that will do it for you…and for FREE.  You simply send them in some easy “code” the rules of your league and the game results and they will simulate the remainder of your season a million times. 

So, for the rest of the season I will be showing updates of the simulated KHL season and playoffs here.

Here is the link to the site for the KHL simulations.  If you play any sport in a league, this can work for you.  Or, use your favorite sports league and give it a try.  If you are a fan of major North American sports, there is a good chance it is already done.  First KHL simulation discussion is up over at EuroHockey.com.

“Za Dom Sportova” Chant – What Happened?

13 Dec

An Apology?  An Explanation?

In the 2nd period of the Ak Bars game on Wednesday (12.11), a few fans began chanting “za dom”.  It appears the fans on the other side of the arena were chanting “sportova”, making the entire chant “za Dom Sportova” (I missed the “sportova” piece, but more on this later).  Dom Sportova is the name of the arena KHL Medvescak plays in, so the chant was “for Dom Sportova”.  This was in response to an announcement of games at the new Arena Zagreb, not the favorite venue of Medvescak fans.

During the 2nd intermission, I put out several tweets condemning the “za dom” chants and calling for fans to shout those “idiots” out.  I also said Medvescak themselves should eject those fans if they really are preaching tolerance.  You can find them over at the Bears Hockey Blog Twitter page.

It was not until later in the game when some of the Bears Blog Twitter followers and the official Medvescak Twitter account made me aware that the full chant was “za Dom Sportova”.

A little background on “za dom”.  It was first used during a play in the 1600′s and translates to “for home” or “for homeland”.  When Croatia was a fascist state during World War II, “za dom” and “za dom – spremni!” (for homeland – ready) became salutes for the fascist army aligned with the Nazi Germans.  Much like the swastika (“swastika” literally translates as “it is good”) which was used as a non-fascist symbol prior to Nazi Germany incorporating it into its ethnic cleansing regime, the phrase “za dom” is now synonymous with fascist activities for most observers.

Recently, Australian-born Croatian National Football team player Josip Simunic led a crowd in Zagreb in a “za dom – spremni” chant after the team punched their ticket to the 2014 World Cup in Brazil.  Simunic stated he was not involved in a racist or nationalist act.  I tend to believe him (others do too).  He was caught up in a moment and that moment had nothing to do with fascism, but helping take your country to the World Cup.

The result:  Fined 3,200 Euros for “spreading racial hatred”, though he was not actually being hateful.

In Latvia, KHL club Dinamo Riga performed a tribute to some of the traditions of Latvian culture during intermission.  One of the most important symbols of Latvian culture is the sun, but their interpretation of what the sun looks like is not always so literal.  So, some skaters paraded on the ice with a “sun” that just happened to look like a swastika.  Initially, the KHL said they respected their traditions and they realized it was not a swastika.

The result:  A reversal.  The KHL fined Dinamo Riga $30,300 (1 million rubles).  They learned that this was the symbol of an military battalion before it was used by Nazi Germany, and not a sun, but also not paying homage to the Nazi regime.  A no tolerance policy.

Points here:

1)       Your actions are not always judged by your intentions.

2)      Like it or not, what the international community think matters and can affect your team.

Let Sleeping Dogs Lie

First off, I run the Bears Hockey Blog site and Twitter accounts.  However, the bearshockeyblog.com consists of several other writers.  I should have never took to the Bears Blog Twitter account to discuss non-hockey issues.  So, to the other writers, I apologize.  Those views were my own.

On the other hand, in the heat of the moment, I felt there was no other way to get my thoughts out and to stop another possible “za dom” chant that evening.  I was trying to harness to power of social media and with that came my feelings at the moment after hearing a fascist chant.

I realize now that the chant with “sportova” added was a play on words, an attempt at humor, mocking the others recently chanting “za dom”.  Well, I should say now that I know “sportova” was being chanted as well I get that it was an attempt at humor.

If I would have heard “sportova” initially, would I have posted those several Twitter messages?

No.

I have a good sense of humor, so I think I would have realized the intention of the fans there.  I would have still thought it was a tasteless joke.  Honestly, I still think anything “za dom” related has no place in a chant or a salute.

But, I did call some people idiots for chanting “za dom”, but since no one was chanting “za dom” but “za Dom Sportova”, then I guess I did not actually call anyone an idiot, right?

It is a dumb argument.  Much like “za Dom Sportova” cannot be offensive to anyone because it was meant to be a joke.  Little known fact: just by adding “sportova” to any phrase gets rid of its negative meaning! (#sarcasm).

Maybe, just maybe, Bears’ fans and Croatians generally can just put this phrase to rest.  It is too volatile and has too much negative connotation to continue to bring up.  Also, why risk Medvescak receiving a fine.  If it happened to Simunic and Dinamo Riga, why could it not happen here because of Wednesday’s incident?  Instead, a local Croatian writer thought it would be smart to bring the situation to light in a recent article, supposedly denouncing my good intention.  I hope no one in the KHL office speaks Croatian…..

Here is a quote from the KHL after the Latvia fine: “”Use of any graphic forms showing Nazi signs and symbols, as well as similar images, are inadmissible for the KHL clubs and their fans.”  Hey FANS, although your chant is not in graphic form, you are jeopardizing the team.

Nevertheless, if you felt I called you an idiot and you were trying to be funny, I sincerely apologize.  This is a small problem that can be solved over a beer or two (on me).  Send me a Twitter message and we can meet during intermission.  @bourciertm.

Also, I apologize to the team’s PR staff for telling them to take action.  It was probably a bit too far.  Though, the club should consider action before this goes too far.

In the meantime, go Bears!

KHL Statistical Power Rankings Explanation

10 Oct

I developed a statistics-based power ranking that will be a weekly feature at EuroHockey.com.  The idea was to come up with a system similar to the BCS ranking for (American) College Football (but less complicated).  Here is the formula and then a part-by-part explanation.

Formula by Team

∑(goal differential per match x opponent points) = RAW

I guess I could write that more formally, but basically here is how it goes.  For each game, I determine the goal differential.  So, If a game is 3-2, then there is a goal differential of 1.  The winning team will get a 1 in the cell for that game.  The losing team will get a 0.

Next, the point differential is multiplied by the number of points the team has in the standings.  Say in the scenario above that each team has 15 points in the standings.  Then the one goal differential is multiplied by 15 and the winning team receives 15 points for that game.  The losing team has 15 multiplied by zero, so teams get no points for the loss.  The totals for all games played are added together for the RAW score.

This means a couple of things.  First, the losing team is not penalized for losing.  Second, the winning team does receive an incentive by beating a team by a larger point margin.  However, just running up the score and not playing defense will not help a team in these rankings, because it is not goals scored, but goal differential.

Overtime and shutout wins are considered indirectly by multiplying these totals by the point standings.  Beating an opponent by the biggest differential who has the highest point standings will give a team the most points for a game.  Beating a lesser opponent is less significant.

The RAW score is adjusted by dividing the number of games played (GP), which gives the “Points Ranking”.

I hope this makes sense and you enjoy the KHL Statistical Power Rankings.  The first edition is here.

Grabovski Hypothesis – Regression Refined (defense)

28 Aug

Regression Refined (Offense)

1st Regression and Post

How does Corsi and Fenwick help in goal prevention?  Here is a first run!

Again, I parceled out blocked shots for (UBS), missed shots for (UMS) and shots against.

More items were statistically significant this time.  Obviously, the less shots you have on goal against you the less you are scored against.  Also, in the obvious column is a keeper’s save percentage–the higher the better.

Yet, running the regression with blocked shots, missed shots, blocked shots for and missed shots for…none of the Corsi and Fenwick stats matter for preventing goals.  The “tilt of the ice”, or possession, doesn’t appear to affect being scored on or scoring against your opponent.  Well…

There is actually a positive coefficient on blocked shots for.  So, if you are being worked by the other team, even if you are blocking shots, you have a better chance of being scored on.  But, it is small.  For every 153 shots a team blocks (on average) they will have one goal scored against them.  This doesn’t suggest a team shouldn’t block shots, but that they shouldn’t be in a position where they have to block shots.  However, this doesn’t seem to mean simple possession is the answer to preventing goals.

What does matter is offensive zone faceoff percentage…the first time we saw this stat.  So, the more you are taking faceoffs in your end, the more you are preventing the opponent from scoring.  This regression says NO?!?  How can that be?

The regression shows that on average, a 16% increase in offensive zone faceoffs leads to one goal against.  I don’t pretend to understand this, but that is what the stats say.

However, this was one of the big assumptions made about Grabovski.  That in Toronto he was getting a lot less offensive zone faceoffs than what he will get in Washington.  The increase in offensive zone faceoffs actually leads to more goals being scored against you.  So, for some reason, offensive zone faceoff percentage is not good for defense and it has no affect on offensive scoring.

Details

2007/08 through 2012/13

Estimate              Std. Error             t value                  Pr(>|t|)

(Intercept)          1.476e+03           2.571e+01           57.412                   <2e-16 ***

SF                           -4.354e-03           2.880e-03            -1.512                    0.1325

SA                           7.691e-02            2.981e-03            25.800                   <2e-16 ***

BS                           -1.455e-03           4.021e-03            -0.362                    0.7180

MS                         -2.449e-03           5.711e-03            -0.429                    0.6687

UBS                        6.544e-03            3.118e-03            2.099                     0.0374 *

UMS                      2.008e-04            5.317e-03            0.038                     0.9699

Sh.                          3.206e-01            2.897e-01            1.107                     0.2700

Sv.                          -1.617e+01          2.812e-01            -57.492                 <2e-16 ***

OZFO.                   6.196e-01            2.492e-01            2.486                     0.0139 *

DZFO.                    6.359e-02            2.003e-01            0.317                     0.7513

east                       -7.814e-01           1.268e+00           -0.616                   0.5387

west                      -8.145e-01           1.349e+00           -0.604                   0.5468

yr13                       -4.903e+00          2.608e+00           -1.880                    0.0619 .

yr12                       -4.675e-01           1.071e+00           -0.437                    0.6630

yr11                       -7.005e-01           1.068e+00           -0.656                    0.5130

yr10                       -2.205e-01           9.284e-01            -0.237                    0.8126

yr9                          -1.085e-01           8.114e-01            -0.134                    0.8938

yr8                          NA                          NA                          NA                          NA

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Grabovski Hypothesis – Regression Refined (offense)

28 Aug

1st Regression and Post

I received a good comment yesterday about whether ‘shots for’ being related to Fenwick and Corsi were messing with the regression.  So, I separated all of that out.

Corsi – Fenwick = Blocked shots (BS)

Fenwick – Shots for = Missed shots (MS)

I re-ran the regression with and without the lockout year from the 07-08 season…..

Results

Missed shots and blocked shots have no statistically significant affect on goals for.  That seems pretty obvious, right?  If a shot is blocked or missed the net, then why would it have an affect?  I think the idea with Fenwick and Corsi “tilting the ice”, or having a lot of possession, does not affect offense statistically according to this regression.  On offense, the only things that seem to matter significantly are shots that actually hit the goalie and shooting percentage.

Again, if a player can individually put more shots on goal or be more accurate than a person is replacing, he would benefit the team.  He could also assist his players by getting them the puck in certain positions to be more accurate or to get the puck on net.  However, it appears the possession portion as defined by Corsi and Fenwick are irrelevant on offense.

More on defense coming soon….

Details

With lockout season….

Estimate              Std. Error             t value Pr(>|t|)

(Intercept)          -98.299082          25.991111            -3.782    0.000218 ***

SF                           0.076925              0.002881              26.704  < 2e-16 ***

SA                           0.003641              0.002532              1.438     0.152307

BS                           0.002288              0.004109              0.557     0.578428

MS                         -0.003707             0.005360              -0.692    0.490186

Sh.                          16.144123            0.307568              52.490  < 2e-16 ***

Sv.                          -0.283125             0.290146              -0.976    0.330600

OZFO.                   0.111255              0.262291              0.424     0.672000

DZFO.                    -0.340800             0.209741              -1.625    0.106112

east                       1.269434              1.335174              0.951     0.343124

west                      1.531076              1.402439              1.092     0.276556

yr13                       0.659509              2.735007              0.241     0.809751

yr12                       -0.013865             1.072697              -0.013    0.989703

yr11                       -0.152273             1.074736              -0.142    0.887503

yr10                       0.352642              0.957111              0.368     0.713017

yr9                          -0.045403             0.849630              -0.053    0.957448

yr8                          NA                          NA                          NA          NA

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Without lockout season….

Estimate              Std. Error             t value                  Pr(>|t|)

(Intercept)          -1.464e+02          1.207e+01           -12.135                 <2e-16 ***

SF                           7.962e-02            1.167e-03            68.241                   <2e-16 ***

SA                          -6.315e-04           1.086e-03            -0.582                    0.562

BS                           2.436e-03            1.658e-03            1.470                     0.144

MS                         -2.519e-03           2.191e-03            -1.150                    0.252

Sh.                          1.827e+01           1.409e-01            129.625                 <2e-16 ***

Sv.                          7.535e-02            1.303e-01            0.578                     0.564

OZFO.                   -9.744e-02           1.195e-01            -0.815                    0.416

DZFO.                    -7.999e-02           9.176e-02            -0.872                    0.385

east                       -1.961e-01           5.760e-01            -0.340                    0.734

west                      -8.845e-02           6.023e-01            -0.147                    0.883

yr13                       NA                          NA                          NA                          NA

yr12                       3.841e-01            4.270e-01            0.900                     0.370

yr11                       3.014e-01            4.280e-01            0.704                     0.482

yr10                       4.671e-01            3.778e-01            1.236                     0.219

yr9                          9.021e-02            3.310e-01            0.273                     0.786

yr8                          NA                          NA                          NA                          NA

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

More on Grabovski – Do advanced stats say anything about a team scoring goals?

28 Aug

An initial disclaimer:

This piece is for discussion.  Statistical operations can be tricky and there can be a number of ways to do things.  I am not claiming to be right or wrong on anything, yet.  If you have some advice, please provide comment.

Round 2

So, after my article on the Caps picking up Grabovski and me not thinking it was as big of a deal as others were making it, the response was brutal.  I take some credit for that by putting out an unpolished piece.  In the end, I stand by my argument that the idea Grabovski would go from a career 45-50 point scorer to a 60-70 point guy was hyperbole.

Some people discussed how his Corsi and Fenwick ratings, and that Washington had a lot more offensive zone faceoffs than Toronto (which should lead to more chances), would make him an improvement over Ribeiro.  I basically argued that despite the improved advanced stats, it seemed crazy that any one person’s numbers would jump that high; thus, the Caps roster is at a net loss without Ribeiro, add Grabo.

To that end, I wanted to examine this further.  Here is my idea:  the better Corsi, Fenwick and offensive zone faceoffs a team has, under the “Grabovski hypothesis”, should lead to more team goals (he manes his teammates better argument).  If this is true, we should be able to perform a linear regression and see how a variety of statistics effect the number of goals a team scores (goals for).  In other words, I wanted to see what happens when we regress a team’s “goals for” for a season (y-variable) on a set of variables, including those mentioned above (X-set).

Thus, I went to stats.hockeyanalysis.com and grabbed team stats for all teams from the 2007-2008 seasons through the last season.  I added all of HA’s data (see legend below) and added some dummy variable, which is common when analyzing panel data.

Legend

TOI = Time on ice
GF = Goals For
GA = Goals Against
GF60 = Goals For per 60 minutes of ice time
GA60 = Goals Against per 60 minutes of ice time
GF% = Goals For percentage = 100* GF / (GF + GA)
SF = Shots For
SA = Shots Against
SF60 = Shots For per 60 minutes of ice time
SA60 = Shots Against per 60 minutes of ice time
SF% = Shots For percentage = 100* SF / (SF + SA)
FF = Fenwick For
FA = Fenwick Against
CF = Corsi For
CA = Corsi Against
Sh% = Shooting Percentage
Sv% = Save Percentage
OZFO% = Percentage of face offs that took place in the offensive zone
DZFO% = Percentage of face offs that took place in the defensive zone

Items in red are in the data table, but were not used in the regression so there weren’t correlation issues between the x-variables.

Dummy Variables

east – Eastern Conference (0=No, 1=yes)

west – Western Conference (0=No, 1=yes)

yr** – year dummy for the year the data was taken (0 = not year **, 1 = year**) – one dummy variable for each of the six years

Results

Looking from the 2007-2008 season through the 2012-2013 season, the regression results only showed statistically significant results (at the 0.05 level) for shooting percentage and shots for (see “regressions results with lockout year” below).

I thought maybe the lockout-shortened season last year might have messed with things a bit, so I removed it and ran it again.  The only thing statistically significant again is shooting percentage and shots for.  Fenwick-for and Corsi-for are statistically significant at the 0.1 level, which is usually not accepted.  Let’s say we do accept the stats at this level.  A team would gain 1.7 goals per season for every additional 1,000 Corsi-for, or 1,000 shots directed at the net, or an one goal per season for every 333 additional Fenwick-for or 333 shots directed at the net (excluding blocked shots).

Grabovski

If I did this correctly, then only those old-fashioned statistics of shots on goal and shooting percentage matter how many times a team scores.  Offensive zone faceoff percentage does not matter.  Corsi and Fenwick are not statistically significant.  Even so, Grabovski and his improvement on other players would have to add 1,000 shots directed at the net to gain an additional 1.7 goals per season (or 333 shots not including blocks).

This does not say whether or not Grabovski will be better or worse than Ribeiro.  But, as it stands, Grabovski’s addition to the team based on the advanced stats do not have a statistically significant affect.  What will matter?  If he can get people the puck to score at a high percentage or put a lot more pucks on net, unblocked.  We know he is not an assist guy, so I think it can be deduced that he will not likely raise the shooting percentage for others (give them good chances).  Ribeiro on the other hand is a distributor based on his higher assist numbers throughout his career.

With the regression, as it is, I think my argument stands….the Washington Capitals roster is worse minus Ribeiro, plus Grabovski.  The boys still have to play this out on the ice….

All files and R script are available upon request.

Details

Regression results with lockout year.

Estimate              Std. Error             t value Pr(>|t|)   

(Intercept)          -9.013e+01          2.739e+01           -3.290    0.00123 **

SF                           7.776e-02            7.542e-03            10.310  < 2e-16 ***

SA                           9.466e-03            7.945e-03            1.191     0.23526

FF                           -4.715e-03           8.897e-03            -0.530    0.59690

FA                           -3.381e-03           7.771e-03            -0.435    0.66408

CF                           2.919e-03            4.285e-03            0.681     0.49663

CA                          -7.803e-04           3.323e-03            -0.235    0.81466

Sh.                         1.614e+01           3.087e-01            52.269  < 2e-16 ***

Sv.                          -3.527e-01           2.997e-01            -1.177    0.24093

OZFO.                   7.607e-02            2.656e-01            0.286     0.77492

DZFO.                    -3.457e-01           2.135e-01            -1.619    0.10732

east                       1.101e+00           1.352e+00           0.815     0.41634

west                      1.385e+00           1.438e+00           0.964     0.33663

yr13                       6.808e-01            2.779e+00           0.245     0.80681

yr12                       1.468e-01            1.141e+00           0.129     0.89776

yr11                       1.178e-02            1.138e+00           0.010     0.99176

yr10                       4.663e-01            9.894e-01            0.471     0.63808

yr9                          1.853e-02            8.647e-01            0.021     0.98293

yr8                          NA                          NA                          NA          NA

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Regression results without lockout season.

Estimate              Std. Error             t value Pr(>|t|)

(Intercept)          -1.444e+02          1.295e+01           -11.152   <2e-16 ***

SF                           8.331e-02            3.073e-03            27.110   <2e-16 ***

SA                          -2.951e-03           3.277e-03            -0.900   0.3696

FF                          -6.418e-03           3.604e-03            -1.781   0.0773 .

FA                           3.601e-03            3.134e-03            1.149   0.2525

CF                           2.929e-03            1.721e-03            1.702   0.0911 .

CA                          -1.713e-03           1.354e-03            -1.266   0.2078

Sh.                          1.826e+01           1.418e-01            128.81   <2e-16 ***

Sv.                          6.188e-02            1.359e-01            0.455     0.6497

OZFO.                   -1.117e-01           1.214e-01            -0.920   0.3592

DZFO.                    -6.648e-02           9.268e-02            -0.717   0.4744

east                       -2.084e-01           5.782e-01            -0.360   0.7191

west                      -2.127e-01           6.114e-01            -0.348   0.7285

yr13                       NA                          NA                          NA          NA

yr12                       5.828e-01            4.596e-01            1.268  0.2071

yr11                       4.903e-01            4.583e-01            1.070  0.2866

yr10                       5.869e-01            3.926e-01            1.495  0.1373

yr9                          1.635e-01            3.373e-01            0.485  0.6286

yr8                          NA                          NA                          NA       NA

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