DATA608: Final Project Proposal

Objective

For my final project, I intend on building a web based application of NHL regular season and post season data to explore:

  • What is most important for a team to advance to the playoffs?
  • Does success in the post season match the metrics from regular season success?

I believe this application can be leveraged be teams to gain insight into where they stack up against the competition and where they can improve to increase their odds of making the playoffs / winning the Stanley Cup.

Methodology

I intend to use R shiny in conjunction with a handful of reactive visualizations (scatterplot, barplot, etc.) to be able to answer the proposed questions above.

Data Sources

The data sources are being acquired from https://www.hockey-reference.com/. As more and more work is done on the project, I may include new data sources to continue progressing on the project. The data needs a lot of clean up before I can start creating visualization. Below is only a small sample size of the datasets I intend to use as I plan to be compiling data from the last 5 to 10 seasons or so.

Regular Season Data

Team Statistics

  • Rk – Rank
  • AvAge – Average age of team weighted by time on ice.
  • GP – Games Played
  • W – Wins
  • L – Losses
  • OL – Overtime/Shootout Losses (2000 season onward)

Scoring

  • PTS – Points
  • PTS% – Points percentage (i.e., points divided by maximum points)

Scoring

  • GF – Goals For

Goalie Stats

  • GA – Goals Against
  • SOW – Shootout Wins
  • SOL – Shootout Losses
  • SRS – Simple Rating System; a team rating that takes into account average goal differential and strength of schedule. The rating is denominated in goals above/below average, where zero is average.
  • SOS – Strength of Schedule; a rating of strength of schedule. The rating is denominated in goals above/below average, where zero is average.
  • GF/G – Goals For Per Game
  • GA/G – Goals Against Per Game

Special Teams

  • PP – Power Play Goals
  • PPO – Power Play Opportunities
  • PP% – Power Play Percentage
  • PPA – Power Play Goals Against
  • PPOA – Power Play Opportunities Against
  • PK% – Penalty Killing Percentage
  • SH – Short-Handed Goals
  • SHA – Short-Handed Goals Against
  • PIM/G – Penalties in Minutes Per Game
  • oPIM/G – Opponent Penalties in Minutes Per Game

Shot Data

  • S – Shots on Goal
  • S% – Shooting Percentage
  • SA – Shots Against
  • SV% – Save Percentage
  • Goalie Stats
  • SO – Shutouts
data_sample <- read.csv("https://raw.githubusercontent.com/SaneSky109/DATA608/main/Final_Project/Data/nhl_2021_2022_stats.csv")

names(data_sample) <- lapply(data_sample[1, ], as.character)
data_sample <- data_sample[-1,]

data_sample
##    Rk                       AvAge GP  W  L OL PTS  PTS%  GF  GA SOW SOL   SRS
## 2   1     Florida Panthers*  27.8 82 58 18  6 122 0.744 337 242   3   4  1.07
## 3   2   Colorado Avalanche*  28.2 82 56 19  7 119 0.726 308 232   4   2  0.91
## 4   3  Carolina Hurricanes*  28.3 82 54 20  8 116 0.707 277 200   1   2  0.88
## 5   4  Toronto Maple Leafs*  28.4 82 54 21  7 115 0.701 312 252   3   1  0.69
## 6   5       Minnesota Wild*  29.4 82 53 22  7 113 0.689 305 249   5   4  0.68
## 7   6       Calgary Flames*  28.8 82 50 21 11 111 0.677 291 206   2   2  0.99
## 8   7  Tampa Bay Lightning*  29.6 82 51 23  8 110 0.671 285 228   2   5  0.64
## 9   8     New York Rangers*  26.7 82 52 24  6 110 0.671 250 204   4   3  0.54
## 10  9      St. Louis Blues*  28.8 82 49 22 11 109 0.665 309 239   2   3  0.79
## 11 10        Boston Bruins*  28.5 82 51 26  5 107 0.652 253 218   2   2  0.38
## 12 11      Edmonton Oilers*  29.1 82 49 27  6 104 0.634 285 251   5   1  0.46
## 13 12  Pittsburgh Penguins*  29.7 82 46 25 11 103 0.628 269 222   3   7  0.49
## 14 13  Washington Capitals*  29.5 82 44 26 12 100  0.61 270 242   5   3  0.35
## 15 14    Los Angeles Kings*    28 82 44 27 11  99 0.604 235 232   4   4  0.03
## 16 15         Dallas Stars*  29.4 82 46 30  6  98 0.598 233 244   5   2 -0.09
## 17 16  Nashville Predators*  27.7 82 45 30  7  97 0.591 262 250   4   2  0.19
## 18 17  Vegas Golden Knights  28.5 82 43 31  8  94 0.573 262 244   4   4  0.21
## 19 18     Vancouver Canucks  27.7 82 40 30 12  92 0.561 246 231   3   5  0.16
## 20 19         Winnipeg Jets  28.2 82 39 32 11  89 0.543 250 253   2   4 -0.04
## 21 20    New York Islanders  30.1 82 37 35 10  84 0.512 229 231   2   6 -0.09
## 22 21 Columbus Blue Jackets  26.6 82 37 38  7  81 0.494 258 297   4   3 -0.44
## 23 22       San Jose Sharks    29 82 32 37 13  77  0.47 211 261   3   3 -0.58
## 24 23         Anaheim Ducks  27.9 82 31 37 14  76 0.463 228 266   4   5 -0.45
## 25 24        Buffalo Sabres  27.5 82 32 39 11  75 0.457 229 287   3   3 -0.69
## 26 25     Detroit Red Wings  26.9 82 32 40 10  74 0.451 227 310   3   2 -0.95
## 27 26       Ottawa Senators  26.6 82 33 42  7  73 0.445 224 264   3   2 -0.44
## 28 27    Chicago Blackhawks    28 82 28 42 12  68 0.415 213 289   6   2 -0.83
## 29 28     New Jersey Devils  25.8 82 27 46  9  63 0.384 245 302   3   5 -0.68
## 30 29   Philadelphia Flyers  28.3 82 25 46 11  61 0.372 210 294   1   4    -1
## 31 30        Seattle Kraken  28.7 82 27 49  6  60 0.366 213 284   3   1  -0.8
## 32 31       Arizona Coyotes    28 82 25 50  7  57 0.348 206 309   1   4 -1.23
## 33 32    Montreal Canadiens  27.8 82 22 49 11  55 0.335 218 317   3   2 -1.15
## 34           League Average  28.2 82 41 32  9  91 0.555 255 255              
##      SOS GF/G GA/G PP PPO   PP% PPA PPOA   PK% SH SHA PIM/G oPIM/G    S   S%
## 2  -0.08 4.11 2.95 64 262 24.43  53  259 79.54 12   8  10.1   10.8 3062   11
## 3  -0.04 3.76 2.83 67 279 24.01  48  236 79.66  6   5     9   10.4 2874 10.7
## 4  -0.05 3.38 2.44 51 232 21.98  33  276 88.04  4   3   9.2    7.7 2798  9.9
## 5  -0.06  3.8 3.07 63 231 27.27  42  234 82.05 13   4   8.6    8.5 2835   11
## 6  -0.02 3.72 3.04 53 258 20.54  63  264 76.14  2   5  10.8   10.8 2666 11.4
## 7  -0.05 3.55 2.51 54 236 22.88  42  250  83.2  7   3   9.1    8.6 2908   10
## 8  -0.02 3.48 2.78 62 259 23.94  49  252 80.56  7   5    11   11.4 2535 11.2
## 9  -0.03 3.05 2.49 55 218 25.23  40  226  82.3  8   2   8.2    8.2 2392 10.5
## 10 -0.05 3.77 2.91 65 241 26.97  35  220 84.09  9   5   7.5    7.9 2492 12.4
## 11 -0.05 3.09 2.66 50 236 21.19  49  262  81.3  5   6   9.9    9.4 2962  8.5
## 12     0 3.48 3.06 61 235 25.96  52  252 79.37 11   6   8.1    7.1 2790 10.2
## 13 -0.04 3.28 2.71 50 248 20.16  33  212 84.43  3   8   6.9    8.4 2849  9.4
## 14 -0.02 3.29 2.95 48 256 18.75  44  225 80.44  8   9   7.7    8.8 2577 10.5
## 15 -0.01 2.87 2.83 40 248 16.13  53  227 76.65 11   9   7.7    8.3 2865  8.2
## 16  0.01 2.84 2.98 54 241 22.41  46  219    79  7   5   6.7    7.5 2486  9.4
## 17  0.02  3.2 3.05 63 258 24.42  59  284 79.23  2   5  12.6   11.9 2439 10.7
## 18 -0.01  3.2 2.98 39 212  18.4  47  208  77.4 10   7   7.6    7.7 2830  9.3
## 19     0    3 2.82 58 247 23.48  58  231 74.89  5   6     8    8.6 2622  9.4
## 20  0.02 3.05 3.09 52 247 21.05  58  232    75  9   8   8.8    9.5 2645  9.5
## 21 -0.02 2.79 2.82 46 208 22.12  34  215 84.19  5   7   8.9    8.4 2367  9.7
## 22  0.02 3.15 3.62 41 220 18.64  51  238 78.57  7   6   7.7    7.2 2463 10.5
## 23  0.03 2.57 3.18 41 216 18.98  33  223  85.2  4  11   8.8    8.6 2400  8.8
## 24  0.03 2.78 3.24 48 219 21.92  43  224  80.8  6   4   9.3    9.8 2393  9.5
## 25  0.02 2.79  3.5 47 222 21.17  50  212 76.42  6   6   8.1    7.9 2451  9.3
## 26  0.06 2.77 3.78 37 227  16.3  59  225 73.78  4  10   8.9    8.5 2414  9.4
## 27  0.03 2.73 3.22 47 243 19.34  49  249 80.32  9   4    10   10.2 2463  9.1
## 28  0.05  2.6 3.52 47 245 19.18  53  223 76.23  2   6   7.9    8.7 2362    9
## 29  0.04 2.99 3.68 35 225 15.56  42  212 80.19  6  14   8.1    8.4 2562  9.6
## 30  0.06 2.56 3.59 30 239 12.55  57  235 75.74  6  11     9      9 2539  8.3
## 31  0.04  2.6 3.46 32 220 14.55  56  223 74.89  8   7   8.5      8 2380  8.9
## 32  0.06 2.51 3.77 28 202 13.86  64  256    75  3   4  10.2    8.2 2121  9.7
## 33  0.05 2.66 3.87 34 248 13.71  67  274 75.55  6  12  10.2      9 2442  8.9
## 34                 49 237 20.61  49  237 79.39  7   7   8.9    8.9 2593  9.8
##      SA   SV% SO
## 2  2515 0.904  5
## 3  2625 0.912  7
## 4  2310 0.913  6
## 5  2511   0.9  7
## 6  2577 0.903  3
## 7  2374 0.913 11
## 8  2441 0.907  3
## 9  2528 0.919  9
## 10 2591 0.908  4
## 11 2354 0.907  4
## 12 2647 0.905  4
## 13 2576 0.914  7
## 14 2378 0.898  8
## 15 2341 0.901  5
## 16 2545 0.904  2
## 17 2646 0.906  4
## 18 2458 0.901  3
## 19 2612 0.912  1
## 20 2721 0.907  5
## 21 2669 0.913  9
## 22 2887 0.897  2
## 23 2622   0.9  3
## 24 2725 0.902  4
## 25 2702 0.894  1
## 26 2761 0.888  4
## 27 2740 0.904  2
## 28 2703 0.893  4
## 29 2540 0.881  2
## 30 2785 0.894  1
## 31 2367  0.88  3
## 32 2910 0.894  1
## 33 2823 0.888  3
## 34 2593 0.902  4

Team Analytics 5v5

  • Rk – Rank
  • S% – Shooting percentage at 5-on-5
  • SV% – Save percentage at 5-on-5
  • PDO – Shooting % + Save %

Corsi (5v5)

  • CF – Corsi For at 5 on 5 Shots + Blocks + Misses
  • CA – Corsi Against at 5 on 5 Shots + Blocks + Misses
  • CF% – Corsi For % at 5 on 5 CF / (CF + CA)
    • Above 50% means the team was controlling the puck more often than not with this player on the ice in this situation. Fenwick (5v5)
  • FF – Fenwick For at 5 on 5 Shots + Misses
  • FA – Fenwick Against at 5 on 5 Shots + Misses
  • FF% – Fenwick For % at 5 on 5 FF / (FF + FA)
    • Above 50% means the team was controlling the puck more often than not with this player on the ice in this situation. Possession Quality
  • xGF – ‘Expected Goals For’ given where shots came from, for and against, while this player was on the ice at even strength. It’s based on where the shots are coming from, compared to the league-wide shooting percentage for that shot location.
  • xGA – ‘Expected Goals Against’ given where shots came from, for and against, while this player was on the ice at even strength. It’s based on where the shots are coming from, compared to the league-wide shooting percentage for that shot location.
  • aGF – Actual goals for (5-on-5)
  • aGA – Actual goals against (5-on-5)
  • axDiff – Actual goal differential minus expected goal differential. A positive differential would indicate a team is converting or stopping an inordinate amount of good chances compared to league average. A negative differential would indicate a team is getting more good chances, but not converting or is allowing more than league norms.
  • SCF – Scoring chances for. Scoring chances are all shot attempts from within a certain range from the net.
  • SCA – Scoring chances against
  • SCF% – Percentage of scoring chances in this team’s favor
  • HDF – High-danger scoring chances for. High-danger chances include shot attempts from the ‘slot’ area and rebounds, approximately.
  • HDA – High-danger scoring chances against
  • HDF% – Percentage of high-danger scoring chances in this team’s favor
  • HDGF – High-danger scoring chances for that lead to goals
  • HDC% – Percentage of high-danger scoring chances that are converted to goals, for this team
  • HDGA – High-danger scoring chances against that lead to goals
  • HDCO% – Percentage of high-danger scoring chances that are converted to goals, for this team’s opponents
data_sample <- read.csv("https://raw.githubusercontent.com/SaneSky109/DATA608/main/Final_Project/Data/nhl_2021_2022_analytics.csv")

names(data_sample) <- lapply(data_sample[1, ], as.character)
data_sample <- data_sample[-1,]

data_sample
##    Rk                        S%   SV%   PDO   CF   CA  CF%   FF   FA  FF%   xGF
## 2   1     Florida Panthers* 8.5 0.919 100.4 4142 3178 56.6 3101 2410 56.3 230.4
## 3   2   Colorado Avalanche* 8.5 0.927 101.2 3847 3411   53 2800 2583   52 187.3
## 4   3  Carolina Hurricanes* 8.2 0.927 100.8 4038 3130 56.3 3112 2456 55.9 189.6
## 5   4  Toronto Maple Leafs* 8.6 0.915 100.2 3980 3469 53.4 3005 2663   53 211.6
## 6   5       Minnesota Wild* 9.3 0.926 101.9 3564 3390 51.3 2702 2563 51.3 185.3
## 7   6       Calgary Flames*   8 0.929 100.9 4108 3263 55.7 3139 2532 55.4 198.9
## 8   7  Tampa Bay Lightning* 8.9 0.925 101.4 3459 3336 50.9 2659 2545 51.1 182.7
## 9   8     New York Rangers*   8  0.93   101 3281 3687 47.1 2483 2756 47.4 162.3
## 10  9      St. Louis Blues* 9.6 0.924   102 3270 3662 47.2 2528 2886 46.7 185.2
## 11 10        Boston Bruins* 6.8 0.923  99.1 3820 3185 54.5 3048 2412 55.8 196.2
## 12 11      Edmonton Oilers* 7.8 0.921  99.9 3891 3515 52.5 2892 2710 51.6 192.5
## 13 12  Pittsburgh Penguins* 7.5  0.93 100.5 3845 3540 52.1 2986 2657 52.9 198.1
## 14 13  Washington Capitals* 8.3 0.922 100.5 3635 3552 50.6 2780 2627 51.4 174.9
## 15 14    Los Angeles Kings* 6.7  0.92  98.7 3866 3284 54.1 2974 2452 54.8 182.9
## 16 15         Dallas Stars* 7.2 0.925  99.7 3538 3690 48.9 2650 2738 49.2 176.6
## 17 16  Nashville Predators* 8.2 0.925 100.7 3431 3495 49.5 2622 2644 49.8 160.5
## 18 17  Vegas Golden Knights 7.6  0.92  99.6 4027 3635 52.6 3011 2594 53.7 204.8
## 19 18     Vancouver Canucks 7.4 0.933 100.7 3616 3667 49.6 2695 2712 49.8 172.2
## 20 19         Winnipeg Jets 7.3 0.927   100 3576 3657 49.4 2741 2793 49.5   188
## 21 20    New York Islanders 7.9  0.93 100.9 3350 3929   46 2503 2922 46.1 175.2
## 22 21 Columbus Blue Jackets 8.5 0.915  99.9 3562 3881 47.9 2677 3038 46.8 162.4
## 23 22       San Jose Sharks 7.1  0.92  99.2 3404 4074 45.5 2576 2930 46.8 161.3
## 24 23         Anaheim Ducks 7.5 0.918  99.3 3451 3799 47.6 2627 2894 47.6 171.2
## 25 24        Buffalo Sabres 7.6 0.914    99 3368 3582 48.5 2583 2854 47.5 158.3
## 26 25     Detroit Red Wings   8 0.914  99.4 3203 3758   46 2414 2910 45.3 172.3
## 27 26       Ottawa Senators 7.2 0.923  99.5 3512 3821 47.9 2640 2852 48.1 162.5
## 28 27    Chicago Blackhawks 7.3 0.917    99 3310 3888   46 2413 2839 45.9 155.8
## 29 28     New Jersey Devils   8 0.905  98.6 3801 3734 50.4 2888 2874 50.1 185.7
## 30 29   Philadelphia Flyers 7.6 0.919  99.5 3403 3895 46.6 2564 2921 46.7 163.6
## 31 30        Seattle Kraken 7.5 0.908  98.3 3508 3415 50.7 2615 2593 50.2 160.5
## 32 31       Arizona Coyotes 8.6  0.91  99.7 3118 3977 43.9 2346 3052 43.5 149.3
## 33 32    Montreal Canadiens 7.6 0.915  99.1 3350 3775   47 2490 2852 46.6 165.6
##      xGA aGF aGA axDiff  SCF  SCA SCF% HDF HDA HDF% HDGF HDC% HDGA HDCO%
## 2  171.1 218 161     -2 2253 1714 56.8 706 597 54.2   90 11.3   64   9.7
## 3  175.6 198 153     33 1885 1729 52.2 544 582 48.3   75 12.1   75  11.4
## 4    163 193 141     25 1944 1647 54.1 666 528 55.8   90 11.9   66  11.1
## 5  168.5 208 182    -17 2168 1803 54.6 629 496 55.9   81 11.4   75  13.1
## 6  153.8 207 150     26 1800 1514 54.3 516 425 54.8   83 13.9   60  12.4
## 7  156.2 196 139     14 2040 1606   56 578 470 55.2   66 10.2   54  10.3
## 8  167.4 183 149     19 1861 1730 51.8 556 510 52.2   77 12.2   70  12.1
## 9  176.3 158 148     24 1618 1853 46.6 494 573 46.3   75 13.2   64    10
## 10 186.7 199 170     31 1835 2002 47.8 501 580 46.3   67 11.8   68  10.5
## 11 143.4 172 144    -25 1839 1476 55.5 588 453 56.5   80   12   71  13.5
## 12 183.5 181 173     -1 1833 1842 49.9 534 528 50.3   86 13.9   67  11.3
## 13 175.9 180 153      5 1900 1650 53.5 550 524 51.2   80 12.7   69  11.6
## 14 166.3 179 157     13 1811 1756 50.8 531 545 49.3   70 11.6   70  11.4
## 15 167.4 155 152    -13 1959 1764 52.6 591 529 52.8   75 11.3   68  11.4
## 16 175.2 147 159    -13 1835 1732 51.4 578 516 52.8   67 10.4   64    11
## 17 170.1 165 156     19 1729 1735 49.9 486 470 50.8   76 13.5   69  12.8
## 18 177.1 183 166    -11 2006 1825 52.4 660 579 53.3   83 11.2   86  12.9
## 19 178.9 156 143     20 1802 1814 49.8 512 525 49.4   60 10.5   62  10.6
## 20 190.1 156 162     -4 1875 1903 49.6 587 606 49.2   72 10.9   66   9.8
## 21 199.1 159 160     23 1744 1963   47 526 602 46.6   74 12.3   66   9.9
## 22 215.4 177 214     16 1685 1960 46.2 489 566 46.4   73   13   71  11.1
## 23   182 142 179    -16 1658 1866   47 548 556 49.6   63 10.3   69    11
## 24 184.8 154 186    -18 1672 1828 47.8 517 542 48.8   72 12.2   61  10.1
## 25   188 158 200    -12 1575 1788 46.8 454 558 44.9   66 12.7   82  12.8
## 26 208.4 159 203     -8 1594 1999 44.4 485 586 45.3   65 11.8   79  11.9
## 27 187.7 147 175     -3 1701 1899 47.3 550 564 49.4   55  9.1   79  12.3
## 28 179.6 143 192    -25 1549 1860 45.4 411 496 45.3   52 11.2   79  13.7
## 29 179.2 181 216    -42 1892 1782 51.5 541 489 52.5   82 13.2   92  15.8
## 30 184.7 159 189     -9 1644 1876 46.7 542 588   48   74   12   76  11.4
## 31 160.8 152 188    -36 1612 1657 49.3 474 514   48   45  8.7   77    13
## 32 208.3 157 221     -5 1542 1972 43.9 480 627 43.4   66 12.1   95  13.2
## 33 199.3 154 195     -7 1671 1987 45.7 544 644 45.8   69 11.3   95  12.9

Playoff Data

The playoff data needs to be web scraped, as there is no one table for all teams in a specific season on the www.hockey-reference.com website. Currently I am working on scraping the data. I have not managed to get it fully running yet, but the general dataframe structure is below. I will aggregate the player and Goalie data to be compared with the regular season data.

Players Data

  • Rk – Rank
  • Age – As of Jan. 31 of the season in question. Or as of the date of the game listed. Age is Years-Days.

Ice Time

  • GP – Games Played

Scoring

  • G – Goals
  • A – Assists
  • PTS – Points

Plus/Minus

  • +/- – Plus/Minus
  • PIM – Penalties in Minutes

Goals

  • EV – Even Strength Goals
  • PP – Power Play Goals
  • SH – Short-Handed Goals
  • GW – Game-Winning Goals

Assists

  • EV – Even Strength Assists
  • PP – Power Play Assists
  • SH – Short-Handed Assists

Shots on Goal

  • S – Shots on Goal
  • S% – Shooting Percentage

Ice Time

  • TOI – Time on Ice (in minutes)
  • ATOI – Average Time on Ice
colorado.players.2022 <- read.csv("https://raw.githubusercontent.com/SaneSky109/DATA608/main/Final_Project/Data/colorado_2021_2022_plyoffs_players.csv")

names(colorado.players.2022) <- lapply(colorado.players.2022[1, ], as.character)
colorado.players.2022 <- colorado.players.2022[-1,]

colorado.players.2022
##    Rk             Player Age Pos GP  G   A PTS +/- PIM EV PP SH GW  EV PP SH
## 2   1         Cale Makar  23   D 20  8  21  29   7  10  5  2  1  1  11 10  0
## 3   2     Mikko Rantanen  25  RW 20  5  20  25   3   4  4  1  0  0   9 11  0
## 4   3   Nathan MacKinnon  26   C 20 13  11  24  11   8  7  6  0  0   8  3  0
## 5   4  Gabriel Landeskog  29  LW 20 11  11  22  15   6  6  5  0  1   7  4  0
## 6   5  Valeri Nichushkin  26  RW 20  9   6  15   6   8  8  1  0  2   6  0  0
## 7   6        Nazem Kadri  31   C 16  7   8  15   7   8  5  2  0  2   8  0  0
## 8   7        Devon Toews  27   D 20  5  10  15   5   8  5  0  0  0   8  2  0
## 9   8   Artturi Lehkonen  26  LW 20  8   6  14   2  20  6  2  0  4   5  1  0
## 10  9        Bowen Byram  20   D 20  0   9   9  15  10  0  0  0  0   8  1  0
## 11 10       J.T. Compher  26  LW 20  5   3   8  -1  10  4  1  0  1   3  0  0
## 12 11   Andre Burakovsky  26  LW 12  3   5   8   3   2  3  0  0  1   4  1  0
## 13 12        Josh Manson  30   D 20  3   5   8   6  12  3  0  0  1   5  0  0
## 14 13    Andrew Cogliano  34  LW 16  3   3   6   5  16  2  0  1  2   2  0  1
## 15 14        Darren Helm  35   C 20  2   3   5   3  12  2  0  0  1   3  0  0
## 16 15       Erik Johnson  33   D 20  1   4   5  10   4  1  0  0  0   4  0  0
## 17 16     Logan O'Connor  25  RW 17  1   3   4   2   9  1  0  0  0   3  0  0
## 18 17       Alex Newhook  21   C 12  0   4   4   0   4  0  0  0  0   3  1  0
## 19 18      Samuel Girard  23   D  7  1   2   3  -1   0  1  0  0  0   1  1  0
## 20 19         Nico Sturm  26   C 13  0   2   2   2   2  0  0  0  0   2  0  0
## 21 20      Darcy Kuemper  31   G 16  0   1   1   0   2  0  0  0  0   1  0  0
## 22 21 Nicolas Aube-Kubel  25  RW 14  0   0   0   2   4  0  0  0  0   0  0  0
## 23 22     Pavel Francouz  31   G  7  0   0   0   0   0  0  0  0  0   0  0  0
## 24 23       Jack Johnson  35   D 13  0   0   0   1  10  0  0  0  0   0  0  0
## 25            Team Total         20 85 137 222 103 169 63 20  2 16 101 35  1
##      S   S% TOI     ATOI
## 2   71 11.3 541 27:04:00
## 3   53  9.4 404    20:11
## 4  117 11.1 428    21:25
## 5   56 19.6 419    20:58
## 6   72 12.5 405    20:16
## 7   58 12.1 278    17:22
## 8   44 11.4 518 25:53:00
## 9   43 18.6 342    17:07
## 10  44    0 387    19:22
## 11  26 19.2 274    13:41
## 12  13 23.1 149    12:25
## 13  39  7.7 344    17:11
## 14  16 18.8 155     9:42
## 15  28  7.1 232    11:35
## 16  30  3.3 340    17:01
## 17  22  4.5 190    11:12
## 18   7    0 129    10:43
## 19  14  7.1 126    17:59
## 20   9    0 110     8:27
## 21   0      887 55:26:00
## 22  16    0 145    10:22
## 23   0      341 48:44:00
## 24   3    0 144    11:06
## 25 781 10.9

Goalie Data

  • GP – Games Played
  • GS – Games Started
  • W – Wins
  • L – Losses
  • T/O – Ties plus Overtime/Shootout Losses
  • GA – Goals Against
  • SA – Shots Against
  • SV – Saves
  • SV% – Save Percentage
  • GAA – Goals Against Average
  • SO – Shutouts
  • MIN – Minutes
  • QS – Quality Starts Starts with SV% > average SV% for the year, or at least 88.5% on nights with 20 or fewer shots against. Developed by Rob Vollman in the Hockey Abstract.
  • QS% – Quality Start Percentage QS/GS > 60% Good, < 50% Bad, ~53% League Avg Developed by Rob Vollman in the Hockey Abstract.
  • RBS – Really Bad Starts Starts with a SV% below 85%. Developed by Rob Vollman in the Hockey Abstract
colorado.goalies.2022 <- read.csv("https://raw.githubusercontent.com/SaneSky109/DATA608/main/Final_Project/Data/colorado_2021_2022_playoffs_goalies.csv")

names(colorado.goalies.2022) <- lapply(colorado.goalies.2022[1, ], as.character)
colorado.goalies.2022 <- colorado.goalies.2022[-1,]

colorado.goalies.2022
##   Rk         Player Age GP GS  W L T/O GA  SA  SV   SV%  GAA SO  MIN QS   QS%
## 2  1  Darcy Kuemper  31 16 16 10 4   0 38 386 348 0.902 2.57  1  887  9 0.563
## 3  2 Pavel Francouz  31  7  4  6 0   0 16 171 155 0.906 2.81  1  341  2   0.5
## 4        Team Total     20    16 4   0 54 557 503 0.903 2.64  2 1228 11  0.55
##   RBS Ga_pct_minus_po Gs_above_avg_po
## 2   3             113            -4.3
## 3   0             107            -1.1
## 4   3             111            -5.3