Halloween 2007 Hash

sh.t trail, awesome area 51 beer stop

I had no idea it was "Plush Animal Lover's Day". telepathy, i guess. some higher entity must be at play here to have made this happen...
I googled "Plush Animal Lover's Day" and all the hits came out as Oct
28, so by internet-applause- meter, it must be correct.

true trail

Megahash 2007

11 mile route

megahash

Buttwipe remembers the 1999 route.

Great Race runner analysis methodology

Copied the results from the website, 100 runners at a time, and pasted them in an Excel spreadsheet. Saved the file in comma delimited format (.CSV). Then, used SQL Server 2000 to import the data.

Massaged and changed some of the data, as to replace NULL values in names, cities, states. Replaced single quotes with empty string.

update RunnerRaw set
Col004 = 'Unknown'
where Col004 IS NULL
go
update RunnerRaw set
Col005 = 'UK'
where Col005 IS NULL
go
update RunnerRaw set
Col008 = 'U'
where Col008 IS NULL
go
update RunnerRaw set
Col002 = (select replace(Col002,'''',''))

update RunnerRaw set
Col003 = (select replace(replace(Col003,'''',''),' ',''))

update RunnerRaw set
Col003 = (select replace(Col003,'Sr.',''))

update RunnerRaw set
Col003 = (select replace(Col003,'Jr.',''))

update RunnerRaw set
Col003 = (select replace(Col003,'St.','St. '))

update RunnerRaw set
Col004 = (select replace(replace(replace(Col004,'''',''),'.',''),',',''))

CREATE VIEW RunnerRawV
AS
select
CAST(Col001 as Integer) as 'Bib',
UPPER(Col002) as 'First',
UPPER(Col003) as 'Last',
UPPER(Col004) as 'City',
UPPER(Col005) as 'State',
UPPER(Col006) as 'Country',
CAST (Col007 as INTEGER) as 'Age',
UPPER(Col008) as 'Gender',
Col009 as 'ChipTime',
Col010 as 'ClockTime',
Cast(SubString (Col009, 1, 1) as Integer) * 3600 + CAST(SubString (Col009, 3, 2) as Integer) * 60 + CAST(SubString (Col009, 6, 2) as Integer) as 'TotalChipSeconds',
Cast(SubString (Col010, 1, 1) as Integer) * 3600 + CAST(SubString (Col010, 3, 2) as Integer) * 60 + CAST(SubString (Col010, 6, 2) as Integer) as 'TotalClockSeconds'
from RunnerRaw

CREATE Table Runner
(
Bib integer NOT NULL,
First varchar (20) NOT NULL,
Last varchar (30) NOT NULL,
City varchar (30) NOT NULL,
State char (2) NOT NULL,
Age Integer NOT NULL,
Gender char (1) NOT NULL,
ChipTime char (7) NOT NULL,
ClockTime char (7) not null,
TotalChipSeconds int not null,
TotalClockSeconds int not null
)

select 'INSERT INTO Runner (Bib, First, Last, City, State, Age, Gender, ChipTime, ClockTime, TotalChipSeconds, TotalClockSeconds) VALUES (' + CAST(Bib AS VARCHAR) + ',''' + LTRIM(RTRIM(First)) + ''',''' + LTRIM(RTRIM(Last)) + ''',''' + LTRIM(RTRIM(City)) + ''',''' + LTRIM(RTRIM(State)) + ''',' + CAST (Age AS VARCHAR) + ',''' + LTRIM(RTRIM(GENDER)) + ''',''' + LTRIM(RTRIM(ChipTime)) + ''',''' + LTRIM(RTRIM(ClockTime)) + ''',' + CAST (TotalChipSeconds AS VARCHAR) + ',' + CAST (TotalClockSeconds AS VARCHAR) + ')'
from RunnerRawV

select State, Replicate ('*', count(State) / 2)
from Runner
where State <> 'PA'
group by State
order by count(State) desc

select avg (totalclockseconds - totalchipseconds) from Runner

select count (First), First
from Runner
where Gender = 'F'
group by First
order by count(First) desc

select distinct First from Runner where Gender = 'F' and First in (select first from Runner where Gender = 'M')

select AVG (CAST (age as float)) from Runner where gender = 'F'

select totalclockseconds - totalchipseconds, *
from Runner
order by totalclockseconds - totalchipseconds desc

select TotalClockSeconds - TotalChipSeconds , * from Runner where TotalClockSeconds - TotalChipSeconds > 400
order by TotalClockSeconds - TotalChipSeconds desc

Great race Runner Analysis

total number of runners: 5550
2566 runners were residents of Pittsburgh city
23 runners were residents of Morgantown city
5 runners were residents of Cleveland city
3 runners were residents of Philadelphia city
0 runners were residents from outside the United States and Terrorties, and Canada

number of men running: 3321
number of women running: 2229

throwing out 5% at both tail's ends:
average finish time of men: 53'03"
average finish time of women: 58'39"

number of runners under age of 14: 80
number of runners under age of 18: 280
number of runners under age of 21: 440 (8% of total running population)

average age of men: 39.4
average age of women: 34.4

age distribution of men

8 *
9 *
10 **
11 **
12 ****
13 **************
14 ************
15 **************
16 ******************
17 *************
18 *********
19 *********
20 **********************
21 **************************
22 ***********************
23 ****************************
24 ********************************
25 **********************************
26 *********************************************
27 *******************************************
28 ***************************************
29 *****************************************
30 *****************************************
31 *****************************************
32 ************************************************
33 *************************************
34 *************************************
35 **************************************
36 ************************************************
37 **********************************************
38 *******************************************
39 **************************************
40 ************************************
41 ***************************************
42 ********************************
43 **********************************
44 ***********************************
45 ************************************
46 ****************************************
47 ********************************
48 **************************************************
49 **********************************************
50 ****************************************
51 *******************************************
52 ***********************************
53 ***************************************
54 **************************************
55 ***************************
56 *************************
57 *********************
58 ****************************
59 ********************
60 *************************
61 ************
62 ************
63 *****
64 ************
65 ********
66 *****
67 *****
68 ***
69 *
70 **
71 *
72 *
74 **
76
80
83
84
99 **


age distribution of women

8
10 *
11
12 ***
13 ********
14 ***********
15 ********
16 ***********
17 ***********
18 ******
19 ************
20 ********************
21 ***********************
22 ****************************
23 *****************************************
24 *********************************************
25 *****************************************
26 ***************************************
27 *******************************
28 ***********************************************
29 *******************************************
30 **********************************
31 ************************************
32 ********************************
33 *****************************
34 **************************************
35 ****************************
36 ********************************
37 *********************************
38 *********************************
39 **************************************
40 **************************
41 ***************************
42 *****************************
43 **************************
44 *********************
45 ******************
46 ***********************
47 ***************
48 ******************
49 *****************
50 ********************
51 ***********
52 **************
53 ***********
54 ***********
55 *********
56 *******
57 ****
58 ***
59 ******
60 *******
61 **
62 *
63
64
65 *
66 *
67 *
68 *
69 *
71
74
75
77
78
99 *

participation of "outa-towners":

OH *********************************************************************
WV **********************************
VA *************************
NY *******************
MD ****************
FL ********
CT *******
NJ ******
NC ******
MA ******
IL *****
DC *****
KY ****
MI ****
GA ****
CA ***
MN ***
AZ **
IN **
SC **
DE **
MO **
TX **
LA *
WI *
OR *
ND
PR
OK
VT
OA
WA
ME
TN
CO
AK
ON
MS
VW
PW
AR

Longest time a runner waited before his shoe with chip crossed the start line: 10 min 14 sec (99% of runners had to wait less than six minutes)

Average time a runner waited before his shoe with chip crossed the start line: 2 min 22 sec

person that most likely was passed by most:
VICTORIA ACKER and EVELINE YORK
both had identical chip time and clock time, meaning they were right at the start line when race started, but arrived 26th from last

person that most likely passed most other runners:
JOSEPH MARTIN, age 31 aka Strap-On, who waited more than 7 minutes before crossing the start line and 7th from last to do so, and then (why I am not surprised...), arrived 469th, more or less passing over five thousand other runners.

Most common last names:
35 SMITH
26 MILLER
20 BROWN
20 JONES
17 THOMAS
17 WILLIAMS
16 WINSCHEL
16 ANDERSON
14 MARTIN
14 MURPHY
14 BAKER
14 CLARK
14 HOFFMAN
13 THOMPSON
12 KELLY
12 KING
10 COOK

Most common first names among men:
95 MICHAEL
86 DAVID
75 JOHN
54 ROBERT
49 CHRISTOPHE
47 JAMES
47 MARK
38 TOM
37 WILLIAM
36 MATTHEW

Most common first names among women:
35 AMY
35 JENNIFER
33 ELIZABETH
29 LISA
26 SARAH
24 SUSAN
24 LAURA
23 KATIE
23 MARY
22 JULIE

first names that are used in both genders:
BRETT
CASEY
CHRIS
CHRISTINE
CHRISTOPHE
DAVE
FRANCIS
JAMIE
JAN
JEAN
KAREN
KELLY
KELLY M
KERRY
KIM
KRIS
LESLIE
MARIA
MICHAEL
MORGAN
PAT
SHELLY
SUSAN
TERRY
TIMOTHY

people whose first name have two Z:
IZZY ICKE

first names with two O:
JOOP
COOKIE
WOODY
LEXIE BROO
KENLEY WOO
BROOKE
COOKIE ARI
GOOD

last names with two Z:
ADDRIZZO
CAPOZZOLI
COZZENS
GALLUZZO
GLIOZZI
GUIZZETTI
LAZZARA
LAZZARIS
MARCOZZI
MAZZEI
MAZZINI
MAZZOCCO
PASCUZZI
PEDAZZOLI
RIZZA
RIZZARDI
ROZZO
RUZZI
SCARAZZO
SPICUZZA

last names with TZ:
ALBITZ
ARETZ
BAMETZRIEDER
BELTZ
BOTZER
CHAMOVITZ
COBLENTZ
COLTZ
DIETZ
DITZLER
FITZGERALD
FITZMAURICE
FITZPATRICK
FRANTZ
GLATZ
GOODYKOONTZ
HARTZELL
HATZO
HETZLER
HOLTZ
HOROWITZ
HORWITZ
HULTZ
KATZ
KRAFTOWITZ
KREITZER
KREUTZER
KURUTZ
LANTZMAN
LEBOUITZ
LEITZE
LEITZEL
LIPETZKY
LIPSITZ
LUTZ
MEDVITZ
MINTZ
MORITZ
MOSKOVITZ
OMATZ
ORNITZ
PEITZ
PELTZ
PITZER
POMERANTZ
RADDATZ
REITZ
RETZLAFF
ROTZ
SCHULTZ
SCHUTZMAN
SCHWEITZER
SETZKORN
SIMITZ
STULTZ
STUTZ
SWARTZLANDER
VOLTZ
WALTZER
WATZLAF
WETZEL
ZELKOWITZ

last names with ICH:
ADAMEROVICH
ALDRICH
BABICH
BASICH
BERKOVICH
BIELICH
CAVRICH
COPERICH
COPICH
DIETRICH
EHRLICH
EICHENLAUB
EICHER
EVANKOVICH
FREUDENRICH
FREUDENRICH III
FROEHLICH
GEORGEVICH
GUREKOVICH
JELOVICH
KNEZEVICH
KRIZMANICH
KUNDICH
MARAVICH
MICHAEL
MICHAELSON
MICHALSKI
MICHEL
MILANOVICH
NICHOLAS
NICHOLSON
NOVAKOVICH
ORLICH
PAVLICH
PERETICH
PEZICH
POSTREICH
QUALLICH
REICH
RESOVICH
RICH
RICHARD
RICHARDS
RICHARDSON
RICHEY
RICHTER
ROTHERMICH
SCLICHTER
SICHER
SICHOK
STANICH
STANKEVICH
STIPANOVICH
TURKOVICH
TWICHELL
WICHMANN
WIDICH
WUKICH
YAKICH
YANOVICH
YURKOVICH

last names with SKI:
ANTOSZEWSKI
BACHOWSKI
BADACZEWSKI
BAGINSKI
BARANOWSKI
BASINSKI
BETHOSKI
BOJARSKI
BORAWSKI
BRONISZEWSKI
CWIKLINSKI
CYTERSKI
DABKOWSKI
DEBOWSKI
DEBSKI
DISKIN
DOBRZYNSKI
DOMBROWSKI
DONAJKOWSKI
FALENSKI
GAJEWSKI
GASKILL
GIELAROWSKI
GOLASHEWSKI
GOLEBIEWSKI
GOODZINSKI
GORALSKI
HUCHROWSKI
JANCZEWSKI
JEGLINSKI
KALKOWSKI
KANSKI
KARLOSKI
KLOSINSKI
KOMARINSKI
KORZENIEWSKI
KOSKI
KOWALSKI
KUMINKOSKI
KUZMKOWSKI
LECZKOWSKI
LESNIAKOWSKI
LEWANDOWSKI
LINKOWSKI
MALESKI
MARKILINSKI
MICHALSKI
MILINSKI-GROSS
MORASKI
MURAWSKI
OLSZEWSKI
ORCHOWSKI
OSEKOWSKI
OSLOWSKI
OSSOWSKI
PASZKOWSKI
PILARSKI
RADKOWSKI
ROSPORSKI
RUSKIN
RUTKOSKI
SASINOSKI
SEKOWSKI
SIKORSKI
SISKIND
SKIDMORE
SKINNER
SOKOLOSKI
SOKULSKI
STARZYNSKI
SULKOWSKI
SWIDZINSKI
TABORSKI
TAMBORSKI
TOMKOWSKI
TRZCINSKI
TWARGOWSKI
ULESKI
WARUSZEWSKI
WINKOWSKI
WINNOSKI
WYZOMIRSKI
ZAGORSKI
ZALENSKI
ZANIESKI
ZAPINSKI
ZATAWSKI
ZUKOWSKI