FlyersPhiladelphia Flyers
9-11-0, 18pts · 12th in Eastern Conference
Player Stats
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Player Name Team Name# POS GP G A P +/- PIM PIM5 HIT SHT OSB OSM SHT% SB MP PPG PPA PPP PKG PKA PKP PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS
1Joe VelenoFlyersFlyers90C206915040185693810.71%920.:.47156000631147.24%58000000.7211
2Patrik LaineFlyersFlyers29LW/RW185914-34067320546.85%218.:.3324600001148.48%3300000.8401
3Marcus FolignoFlyersFlyers17LW/RW18561112810575921328.47%420.:.54314000701061.54%3900000.5801
4Miles WoodFlyersFlyers28LW206511-21953649132412.24%520.:.04123000531256.00%2500200.5500
5Andreas AthanasiouFlyersFlyers89C/LW/RW203710-30004014347.50%314.:.1101100001041.82%22000000.7101
6Matt BenningFlyersFlyers5D18189512036155176.67%2919.:.5901100060000.00%000000.5000
7Michael CarconeFlyersFlyers88LW20369-52024915356.12%114.:.4322400000036.84%1900000.6100
8Tyson BarrieFlyersFlyers44D20268-136019258218.00%2820.:.192240004000.00%000000.3900
9Jackson LaCombeFlyersFlyers60D20347-914051359238.57%5525.:.3621300067000.00%000000.2700
10Brendan LemieuxFlyersFlyers22LW1941544013135330.77%009.:.1300000001046.67%1500000.5700
11Liam O'BrienFlyersFlyers98C20134-412036219134.76%513.:.37000000771042.86%40600000.2900
12Nicolas DeslauriersFlyersFlyers20LW20134-6100261810145.56%210.:.0501100000130.77%2600000.4000
13Keegan KolesarFlyersFlyers55RW20134-3180414315382.33%114.:.2901100060050.00%2200000.2800
14Ryan JohansenFlyersFlyers92C9134-2002297163.45%217.:.2102200001055.61%22300000.5101
15Vincent DesharnaisFlyersFlyers73D2013402554111479.09%2917.:.5000000067000.00%000000.2200
16Tomas NosekFlyersFlyers24C/LW303320013030.00%011.:.1400000000054.55%2200001.7800
17Austin WatsonFlyersFlyers25C/LW/RW20123-1221029204175.00%210.:.22000000120042.71%19900000.2900
18Matt GrzelcykFlyersFlyers48D13033-1201810160.00%1714.:.2300000017000.00%000000.3200
19Kyle BurroughsFlyersFlyers38D20123128069910611.11%2516.:.4600000058010.00%000000.1800
20Ryan LombergFlyersFlyers94LW/RW7112-3001941011.11%109.:.1200000000050.00%400000.6200
21Adam EdstromFlyersPhantoms (PHI)84C/LW/RW200000003120.00%010.:.0600000010033.33%600000.0000
22Ben HarpurFlyersFlyers2D200010001200.00%312.:.320000003000.00%000000.0000
23Andreas EnglundFlyersFlyers3D5000120164240.00%417.:.210000004000.00%000000.0000
24Byron FroeseFlyersPhantoms (PHI)11C300000005010.00%108.:.5800000000033.33%2700000.0000
25Louis CrevierFlyersPhantoms (PHI)46D3000-12091010.00%516.:.330000007000.00%000000.0000
Team Total or Average3524687133-40212305185921874157.77%22716:241323360005698646.37%183300200.4615
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Goalie Name Team NameGP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA ST BG S1 S2 S3
1Martin JonesFlyersFlyers1578091.4%3.249082049568000100.0%5155120
2Kaapo KahkonenFlyersFlyers523092.3%2.2129940111430000.0%0515010
Team Total or Average20911091.6%2.98120760607110001.00052020130
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# VS Team GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT GF% SH% SV% PDO PDOBRK
1FlyersHurricanes2200000063341.00061218004020602217210731830466233.33%14285.71%034769949.64%37482945.11%14031444.59%41255017281280.0%10.0%95.9%105.9LUCKY
2FlyersLightning1010000034-100.00036900111038813170501926263133.33%6266.67%034769949.64%37482945.11%14031444.59%181128813650.0%7.9%92.0%99.9DULL
3FlyersCapitals2110000057-220.5005914001220592712200651822566233.33%5340.00%034769949.64%37482945.11%14031444.59%42264916281342.9%8.5%89.2%97.7Unlucky
4FlyersPanthers1010000005-500.00000000000021858051202232000.00%11463.64%034769949.64%37482945.11%14031444.59%1692981350.0%0.0%90.2%90.2Unlucky
5FlyersBruins2020000049-500.0004812003100531914200692212466233.33%5180.00%034769949.64%37482945.11%14031444.59%43284815271320.0%7.5%87.0%94.5Unlucky
6FlyersSenators1100000032121.000369000300208840301315303133.33%5180.00%034769949.64%37482945.11%14031444.59%171028913566.7%15.0%93.3%108.3LUCKY
7FlyersCanadiens1000100043121.0004812001111391271554916934200.00%20100.00%034769949.64%37482945.11%14031444.59%241625713657.1%10.3%93.9%104.1LUCKY
8FlyersSabres1010000036-300.000358003000298156053158343266.67%4175.00%034769949.64%37482945.11%14031444.59%191328712516.7%10.3%88.7%99.0FUN
9FlyersBlues1010000001-100.00000000000033715110286818800.00%40100.00%034769949.64%37482945.11%14031444.59%22132481360.0%0.0%96.4%96.4DULL
10FlyersWild1100000053221.0005101500122028101080371814292150.00%7271.43%034769949.64%37482945.11%14031444.59%221524713680.0%17.9%91.9%109.7LUCKY
11FlyersOilers1010000012-100.00012300010022571002766256116.67%30100.00%034769949.64%37482945.11%14031444.59%19112781360.0%4.5%92.6%97.1DULL
12FlyersFlames1010000023-100.000246002000236125027101620300.00%8187.50%034769949.64%37482945.11%14031444.59%211426712550.0%8.7%88.9%97.6Unlucky
13FlyersCanucks2000101075241.000711180011328323232913672110518112.50%5260.00%034769949.64%37482945.11%14031444.59%52354715281466.7%8.4%92.5%101.0DULL
14FlyersAvalanche1010000003-300.000000000000307101303515829300.00%40100.00%034769949.64%37482945.11%14031444.59%20122781260.0%0.0%91.4%91.4DULL
15FlyersSharks1010000002-200.000000000000351110140238820300.00%3166.67%034769949.64%37482945.11%14031444.59%24162271260.0%0.0%91.3%91.3DULL
16FlyersKraken1100000042221.000461000103028513100278631100.00%30100.00%034769949.64%37482945.11%14031444.59%221322814766.7%14.3%92.6%106.9LUCKY
_Vs Division431000001110160.7501121320052401194929410138365210212433.33%19573.68%034769949.64%37482945.11%14031444.59%835210033562658.3%9.2%92.8%102.0LUCKY
_Vs Conference1146010002839-11100.4552854820013861319112911115440141144304291034.48%521473.08%034769949.64%37482945.11%14031444.59%223142289901496841.9%8.8%91.1%99.9Unlucky
_Since Last GM Reset20611020104760-13180.450478713400181214360118619121118711233220527631320.63%892077.53%034769949.64%37482945.11%14031444.59%42927651216127012945.9%7.8%91.6%99.4DULL
Total20611020104760-13180.450478713400181214360118619121118711233220527631320.63%892077.53%034769949.64%37482945.11%14031444.59%42927651216127012945.9%7.8%91.6%99.4DULL

Puck Time
Offensive Zone 21
Neutral Zone 13
Defensive Zone 25
Puck Time
Offensive Zone Start 699
Neutral Zone Start 314
Defensive Zone Start 829
Puck Time
With Puck 28
Without Puck 32
Faceoffs
Faceoffs Won 861
Faceoffs Lost 981
Team Average Shots after League Average Shots after
1st Period 9.310.69
2nd Period 18.921.35
3rd Period 29.432.03
Overtime 30.332.64
Goals in Team Average Goals after League Average Goals after
1st Period 0.91.08
2nd Period 1.52.13
3rd Period 2.22.97
Overtime 2.43.07
Even Strenght Goal 34
PP Goal 13
PK Goal 0
Empty Net Goal 0
Home Away
Win 45
Lost 65
Overtime Lost 00
PP Attempt 63
PP Goal 13
PK Attempt 89
PK Goal Against 20
Home
Shots For 30.1
Shots Against 35.6
Goals For 2.4
Goals Against 3.0
Hits 26.4
Shots Blocked 11.7
Pim 11.0