FanPost

Devils' Goal Scorers and overall GF/GA performance: 2014-2015

In this FanPost, I used mathematical trend analyses of goalscoring for all of the forwards to predict their performance for the 2014-2015 season. Then, I looked at all of the defensemen, and computed how much the Devils can expect to score. Finally, I looked at goaltending, and I used this information to predict how the Devils, as a team, will do next season. All of my information came from Hockey Reference.

For those of you who just wish to know my results...

-Henrique is the leading scoring with 29, leading the Devils to 202 GF. Zidlicky remains the top offensive defensemen, contributing 11 goals.
-Lead by Cory's stellar goaltending, the Devils give up a measly 178 GA.
-This leads to an elite differential of +24 Goals.

My study is below. Thanks for reading up to here and beyond!

For determining goal contribution for the 2014-2015 season, I looked at the G/GP of each player over their career. I used this data and graphed a line of best fit to predict where we can expect the Devils' players to be for next season. The example of Jagr is below. Click on the picture to zoom in.

Screen Shot 2014 07 04 at 10 19 17 PM

Graphing his G/GP over his career, I used a 2nd degree polynomial line of best fit to determine where we could expect him to be next season. His 2014-2015 expected G/GP is 0.175. I also looked at his average GP over his career, 73.65, and I used this number and other information to predict his GP for 2014-2015: 80 GP. I repeated this process for 16 of our forwards. The data from this analysis follows.

Forwards

Player GP (ave.) Formula for G/GP with variable year Year of Q Exp. G/GP 2014 Exp. GP 2014 Exp. G 2014
1 Henrique 64.33 y = 100.6ln(x) - 764.97 2014 0.367 80 29
2 Ryder 75.9 y = 0.0003x^2 - 1.0298x + 1038.2 2014 0.291 78 23
3 Cammalleri 60.82 y = -0.0034x^2 + 13.695x - 13755 2014 0.286 63 18
4 Elias 67.88 Y=-.0025X^2+9.8413X-9862.9 2014 0.23 76 17
5 Zajac 68.88 y = -6E-05x^2 + 0.2341x - 229.88 2014 0.175 80 14
6 Brunner 60 2014 0.22 62 14
7 Jagr 73.65 y = -0.0016x2 + 6.5521x - 6546.9 2014 0.175 80 14
8 Rutuu 59.83 y = -0.0023x^2 + 9.0974x - 9130.1 2014 0.138 59 8
9 Zubrus 68.76 y = -0.0019x^2 + 7.6941x - 7712.4 2014 0.079 71 6
10 Havlat 57.5 y = -0.0037x^2 + 14.725x - 14760 2014 0.086 59 5
11 Bernier 60.22 y = 0.0043x^2 - 17.122x + 17233 2014 0.081 62 5
12 Gionta 66 2014 0.07 68 5
13 Boucher 23 2014 0.09 41 4
14 Clowe 57.5 y = -0.0058x^2 + 23.425x - 23517 2014 0.051 57 3
15 Josefson 29.5 y = 0.0125x^2 - 50.309x + 50619 2014 0.073 31 2
16 Matteau 17 2014 0.06 17 1
2014 2.472 984 168

Notes:
-Some players polynomial lines of best fit did not make sense, so I used other functions like 'log'.
-The predicted GP did not reach the expected (12*82=984) so I made the following GP additions: 2 to each player, Zajac: 5, Henrique: 3, Elias: 4, Boucher: 15, Jagr: 3.
-I removed the first year of data from the following players: Henrique, Elias, Clowe
-Some players had such small sample sizes, I had to use other methods to determine their prediction.

-I assumed 12 Forwards played a game.

Defense

Player GP (ave.) Formula for G/GP with variable year Year of Q Exp. G/GP 2014 Exp. GP 2014 Exp. G 2014 Remove first Year?
1 Zidlicky 69.9 y = 0.0022x2 - 8.9489x + 8990.3 2014 0.139 80 11
4 Gelinas 60 2014 0.12 72 9 Only 1 year data
2 Greene 59.7 y = 4E-93e0.1044x 2014 0.078 82 6
5 Larsson 42.67 y = 10.054ln(x) - 76.458 2014 0.033 80 3
6 Merrill 52 2014 0.04 80 3 One year
3 salvador 64.25 y = 2E-05x2 - 0.0649x + 67.727 2014 0.012 67 1
7 Harrold 28.88 y = -2.627ln(x) + 20.016 2014 0.033 31 1

Notes:
-Additional GP: Zidlicky: 10, Greene: 12, Salvador: 3, Gelinas: 12, Larsson: 37, Merrill: 28, Harrold: 2.
-Some players had such small sample sizes, I had to use other methods to determine their prediction.
-I assumed 6 defensemen played a game.

This process determined that the Devils should score a pedestrian 202 goals next season. This is 4 more than last year.

Now, I used a similar process to analyze our goaltending.

Goaltending

Player GP (ave.) Formula for SV% with variable year Year of Q Exp. SV% 2014 Exp. GP Exp. SA/G Ex. GA
1 Schneider 27 y = 2.0139ln(x) - 14.393 2014 0.926 64 26 123
2 Clemmensen 20.2 y = -0.0013x2 + 5.373x - 5398.9 2014 0.881 9 26 28
3 Kinkaid 1
2014 0.885 9 26 27
82 178

Notes:
-I guessed GP for all three goaltenders.
-I guessed a SV% for Kinkaid of 88.5%

-Devils' average SA/G since 2009 is 24.13. I used 26 SA/G (0.5 more than last season) to be reasonable.

Findings and Disclaimer

This method shows that we should give up an minuscule 178 GA next season. This, combined with 202 GF leaves the Devils with an elite goal differential of +24. This is a fantastic result! I think the most interesting part of this is the impact that having elite goaltending (again) will have on the Devils.

I do not believe that this process is entirely mathematically sound. The idea of a line or curve of best fit is a good idea, but there is no way that it is a perfect predictor for goal scoring (this problem would be compounded more the further into the future one would try to predict). I also did not weight averages based on how many games were played by a player in a single season. Furthermore, TOI was not a factor in my calculations. All of this being said, I think that in principle, my analysis is applicable. At the least, it can be a cause for conversation.

Your Take

Obviously, Jagr being significantly out-performed by Ryder is concerning (and there are other problems unmentioned and mentioned in the previous section), but I think that overall this is a good way to analyze goal-scoring trends.

What do you guys think of this analysis? Are these predictions way off? Can Cory perform at that level? Which players will break away from their trends? Leave your comments below, and thank you so much for reading.
-Brogamesh

All FanPosts and FanShots are the respective work of the author and not representative of the writers or other users of In Lou We Trust.

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