Monday, May 3, 2010

Miss Match Up Mashup: April 2010 MLB Edition



By Spezzal Teams Playa


Last season, our baseball league ran a regular rundown of the best performances of the week. The feature was called Most Offensive Line and highlighted the best five bats, according to our league settings. I teamed those picks with the best five arms (Pitchers Worth a Thousand Words), the worst five arms (Belly-Itcher Citations), and the worst five bats (No Offence Man Citations).

I had started by publishing nightly reports of these 20 players, but found that it was too much of a commitment, so I dropped them to a weekly basis.

This year, I thought it would be easier to only feature about 11 or 12 players and once again attempted to provide nightly reports on these stars, in a feature I called Box Score'd. When that again proved impossible (duh), I decided to come up with a monthly report. Since a once-a-month article didn't seem like much work, I decided that it'd spotlight 70 players and got to work on constructing a table.

Well that was like, almost three days ago.

Now that I've finally figured out how to use this internets, I present to you a new feature which purports to answer the question of what would the most lopsided Matchup have looked like if, ya know, we played monthly Matchups and stuff.

So I gathered the best April performers and placed them on the All-Star Team, then gathered the worst performers and called their group the Bad News Bears. Think of it like a Harlem Globetrotters versus Washington Generals lineup card. From there, I crowned one of the All-Stars the winner of this digital pageant (basically, the Player of the Month), while also singling out the worst performer of all.

  • Phase 1: Have a look to see where your guys rank
  • Phase 2: Spot the buy-low targets
  • Phase 3: Profit




MLB All-Stars

LineupRankPlayerPosClubOwnerORankOwnedABRHHRRBIBBSBSB%FPCTDPTPOAOFAAVGOPS
C120Miguel OlivoCCldBastards47964%551016513411.0001.000011513-.291.933
1B9Miguel Cabrera1BDetStittsville1198%961633525130.000.9912721414-.3441.042
2B1Robinson Cano2BNYYDeadwood4398%85213481862.500.991174074-.4001.201
3B5Evan Longoria3BTBIsle Wynn798%882230518103.600.94482245-.3411.002
SS25Derek JeterSSNYYDeadwood3498%94143141833.750.975123247-.330.875
LF3Ryan BraunLFMilDeadwood599%9319335201261.000.97403711.3551.011
CF6Vernon WellsCFTorFranconia18593%922031816811.0001.00015511.3371.113
RF7Matt KempCF/RFLADFatMonkeyButz699%97202772083.375.98105300.278.879
BN
BN11Paul Konerko1BCWSFatMonkeyButz27773%7413221121150-.9941515810-.2971.197
BN14Kelly Johnson2BAriSunnyvale88975%801725918130-.976112953-.3131.154
BN17Carl CrawfordLFTBSunnyvale1998%89173021397.7001.00004044.337.941
BN19Colby RasmusCF/RFStLEl Paso19080%651921612173.600.97603911.3231.171
BN21Jose GuillenRFKCColt 4596382%921428719311.0001.0000200.304.946
BN24Mark Reynolds1B/3BAriIsle Wynn3897%7918197211411.000.96722236-.241.909
BN31Alex GonzalezSSTorIsle Wynn109567%971428719311.000.965213179-.289.946
BN123Jorge PosadaCNYYIsle Wynn16592%58111851270-.98101023-.3101.032
DL
DL15Nelson CruzRFTexGolden Carp6396%6212207171051.0001.00014311.3231.177
DL179Manny RamirezLFLADFowl Play8993%4171721290-1.00001700.4151.159



Lineup RankPlayerPosClubOwnerORankOwnedIPQSWSVSVOPNSVHLDKK/BBK/9H/9BB/9ERAWHIP
SP2Tim LincecumSPSFFranconia1499%35.154----436.1410.955.6041.7831.270.82
RP29Tyler ClippardRPWshKoo Koo Roos25450%18.0-301-16232.5611.503.5004.5000.500.89
RP46Matt CappsRPWshMuff Divers43479%13.1-0101010-152.5010.138.1004.0500.681.35
RP51Mariano RiveraRPNYYFatMonkeyButz8398%9.0-0777-94.509.003.0002.0000.000.56
RP52Kevin GreggRPTorFowl Play39472%11-066611414.0011.454.9090.8180.820.64
RP65Leo NunezRPFlaSunnyvale19584%9.1-1453-102.009.64.9644.8210.000.64
RP67Carlos MarmolRPChCBastards11996%11.2-1342-224.4016.975.4003.8570.771.03
BN
BN4Ubaldo JimenezSPCldIsle Wynn7198%34.155----312.218.135.7673.6700.791.05
BN8Roy HalladaySPPhiSTL Birds2099%40.044----3311.007.438.1000.6751.800.98
BN10Barry ZitoSPSFPerrysburg45280%35.154----242.186.115.0942.8021.530.88
BN12Adam WainwrightSPStLCT Defenders6198%38.054----303.757.116.1581.8952.130.89
BN16Francisco LirianoSP/RPMinMuff Divers102186%29.043----272.708.385.5863.1030.930.97
BN18John DanksSPCWSDakota Magic11193%29.043----264.338.075.8971.8621.550.86
BN71David AardsmaRPSeaChesapeake27188%9.2-0897-113.6710.243.7242.7932.790.72
DL
DL149Brett AndersonSPOakSTL Birds12379%23.031----174.256.657.8261.5652.351.04
DL190Chris YoungSPSDSTL Birds34523%6.011----51.677.501.5004.5000.000.67
DL192Jorge De La RosaSPCldPerrysburg19369%23.013----262.0010.177.8265.0873.911.43





Bad News Bears

LineupRankPlayerPosClubOwnerORankOwnedABRHHRRBIBBSBSB%FPCTDPTPOAOFAAVGOPS
C1433A.J. PierzynskiCCWSHell Lions33427%654110430-1.00001416-.169.429
1B1093Matt LaPorta1B/LF/RFCleFatMonkeyButz11164%605130160-1.00013860.217.538
2B1406Ian Kinsler2BTexCT Defenders1698%5010000-1.000142-.200.400
3B1283Pedro Alvarez3BPitSTL Birds13992%---------------
SS1393Brendan Ryan2B/SSStLDakota Magic7513%675120490-.978142463-.179.515
LF1104Matt DiazLF/RFAtlKoo Koo Roos3323%462902211.000.94101600.196.490
CF1408Ben FranciscoLF/CF/RFPhiBastards3401%18040110-1.0000511.222.578
RF1174Michael StantonRFFlaSTL Birds13843%---------------
BN
BN1095David Ortiz1BBosMuff Divers12551%56581470.000-----.143.524
BN1314Brett Wallace3BTorDakota Magic14071%---------------
BN1407Jayson Nix2B/3B/SSCWSColt 4510730%15130000-.923066-.200.400
BN1415Garrett AndersonLFLADFowl Play4501%41251420-1.00001011.122.379
BN1439Dioner NavarroCTBKoo Koo Roos10691%44460240-.9890817-.136.367
BN1442Julio Lugo2B/SSBalKoo Koo Roos10201%282300211.000.97451720-.107.274
BN1448Randy WynnLF/CF/RFNYYFranconia2259%13010000-1.0000700.077.154
BN1449Eric ByrnesLFSeaCT Defenders10900%281300611.0001.00002400.107.444
DL
DL1114Mike CameronCFBosIsle Wynn23016%30270050-.96402611.233.694
DL1353Carlos BeltranCFNYMSTL Birds15968%---------------
DL1131Coco CrispCFOakSTL Birds10431%---------------
DL1208Freddy Sanchez2BSFBastards26611%---------------
DL1232Jose MoralesCMinHell Lions11980%---------------


Lineup RankPlayerPosClubOwnerORankOwnedIPQSWSVSVOPNSVHLDKK/BBK/9H/9BB/9ERAWHIP
SP1462Nick BlackburnSPMinFranconia27417%23.211----70.882.6612.5413.0426.851.73
RP978Trevor HoffmanRPMilPerrysburg25372%9.0-137-1-51.605.0015.0003.00013.002.00
RP979Rafael PerezRPCleColt 459810%7.0-0----51.005.5515.4296.4295.142.43
RP987Jason FrasorRPTorCT Defenders27621%9.2-03512131.6312.1015.8287.4488.382.59
RP1023Randy WilliamsRPCWSGolden Carp6770%9.2-001-1-90.648.3810.24113.0344.662.59
RP1026Danys BaezRPPhiLightning Bolts4511%9.0-002-2240.804.0010.0005.0009.001.67
RP1032Nick MassetRPCinFowl Play3233%10.1-2---1172.1314.8114.8066.96811.322.42
BN
BN1463John LannanSPWshKoo Koo Roos4623%27.221----100.673.2511.7114.8805.531.84
BN1464Kyle KendrickSP/RPPhiMuff Divers7491%23.210----121.204.5611.0283.8037.611.65
BN1465Josh BeckettSPBosMuff Divers8896%28.221----201.546.2811.6164.0817.221.74
BN1466Rick PorcelloSPDetStittsville14063%24.212----162.675.8414.9592.1898.031.91
BN1470Jake PeavySPCWSFowl Play13880%28.210----221.106.9110.0476.2797.851.81
BN1473Gil MecheSPKCLightning Bolts8871%18.200----120.805.7913.0187.23210.132.25
BN1053Scot ShieldsRPLAAColt 456130%6.0-001-1050.637.5012.00012.00010.502.67




And the player adjudged to have hurt his owner more than any other, singled out as the Great Goat is:

Gil Meche




Which means the winner of the month and your newly crowned Miss Match Up is:

[drum roll ....]


Robinson Cano




7 comments:

  1. I don't even know how this will look on different monitors. Please leave me a comment telling me if the font is too small, or if you can even see all the columns, like AVG & OPS, or ERA & WHIP.

    I obviously put too much data into one table, but I had passed the point of no return by the time I realized it.

    ReplyDelete
  2. The table doesn't format right - logos are over cells and there's just too much information in there.

    The concept is great - and I'd suggest changing to simply player - position - team - Stat Summary (just highlight the positives or negatives).

    ReplyDelete
  3. The logos? You mean the All-Star logo and the Bad News Bears logos? I purposely left all graphics out of the table to avoid just that.

    Damn this new widescreen monitor I got. Last month I was double-checking on my old desktop with a normal-shaped monitor, but now I only have the one screen at home. I'll take a look at work tomorrow.

    The real issue, as you point out, is that there is simply too much information. I wanted to include all 15 scoring cats and each players' rank now and in the pre-season, then I figured why not show how owned each guy is and before I knew it, my table was way too wide.

    By then, I didn't feel like going back and ripping out columns, due to the incredible clunkiness of Blogger.

    You're gonna hafta show me some tricks.

    ReplyDelete
  4. Anyone else experiencing similar problems?

    Logos overlapping onto the table, the last few columns cut off (AVG, OPS, or ERA, WHIP), unreadable font that has been squished together too much?

    ReplyDelete
  5. only prob i see is that a few cats got cut off. outside of that looks great! As an ego centric side note...i have only one player out of 70 on the list. Is that the key to success be mediocre at all positions?

    ReplyDelete
  6. So which columns should I get rid of so the May feature will contain some of the minor cats like, oh say AVG, OPS, ERA and WHIP.

    Idiot that I am, I chose those as the most important stats and assigned them the right-hand side of the page to make them more visible.

    I'm thinking I drop O-Rank and %Owned, as well as Position and maybe combine Player and Name to streamline it. Maybe even just go with a first initial.

    ReplyDelete
  7. Columns that seem to clutter up the table that can be removed:

    Rank, Either Pos of Lineup, ORank, Owned

    For hitters - these can be removed without losing much "critical" data:

    AB, H, SB%, OFA, OPS

    (No one is making the list based on AB's or OFA) - the H, SB% and OPS are already represented by the other columns.

    For Pitchers:

    NSV, K/BB, k/9, BB/9, H/9 can be removed as the WHIP and K columns already provide that data (in another way)...

    ReplyDelete