This repository builds an Sqlite3 database with results from World Rowing Championships.
| Year | Event |
|---|---|
| 2010 | WCH Karapiro, New Zealand |
| 2011 | WCH Bled, Slovenia |
| 2012 | 2012 WSJCH Plovdiv, Bulgaria |
| 2013 | WCH Chungju, Korea |
| 2014 | WCH Amsterdam, NED |
| 2015 | WCH Aiguebelette, France |
| 2016 | WCHIE Rotterdam, Netherlands |
| 2016 | WCH Rotterdam, Netherlands |
| 2017 | WCH Sarasota-Bradenton, USA |
$ makeIf you don't have Make available, you can also load world-cup.sql into
an Sqlite3 datbase to create the database from the CSV files in this
repository.
Find the fastest races in selected boat classes for every year:
SELECT year,
class,
min(time_2000m) AS time,
team,
descr
FROM race,
crew
WHERE class IN ( "M8+", "W8+", "M4x", "W4x" )
AND crew.race = race.id
GROUP BY class,
year
ORDER BY class,
time,
year; Result times are in seconds.
year class time team descr
---- ----- ------ ---- ---------------------------
2014 M4x 332.26 UKR WCH Amsterdam, NED
2011 M4x 339.31 AUS WCH Bled, Slovenia
2013 M4x 341.13 GER WCH Chungju, Korea
2010 M4x 341.44 CRO WCH Karapiro, New Zealand
2017 M4x 343.1 LTU WCH Sarasota-Bradenton, USA
2015 M4x 343.32 GER WCH Aiguebelette, France
2014 M8+ 324.11 GBR WCH Amsterdam, NED
2010 M8+ 324.62 GER WCH Karapiro, New Zealand
2015 M8+ 325.28 NED WCH Aiguebelette, France
2017 M8+ 326.85 GER WCH Sarasota-Bradenton, USA
2011 M8+ 328.81 GER WCH Bled, Slovenia
2013 M8+ 330.35 GBR WCH Chungju, Korea
2014 W4x 366.84 GER WCH Amsterdam, NED
2015 W4x 375.24 GER WCH Aiguebelette, France
2010 W4x 375.62 GBR WCH Karapiro, New Zealand
2017 W4x 376.71 NED WCH Sarasota-Bradenton, USA
2011 W4x 378.37 GER WCH Bled, Slovenia
2013 W4x 383.27 POL WCH Chungju, Korea
2014 W8+ 356.83 USA WCH Amsterdam, NED
2015 W8+ 359.58 USA WCH Aiguebelette, France
2010 W8+ 360.52 USA WCH Karapiro, New Zealand
2013 W8+ 362.14 USA WCH Chungju, Korea
2011 W8+ 363.65 USA WCH Bled, Slovenia
2017 W8+ 366.4 ROU WCH Sarasota-Bradenton, USA
Compare the fastest race for a boat class in a year between light-weight and open-weight crews.
select
A.year,
A.class,
B.class,
printf( "%5.2f", (B.time / A.time - 1.0) * 100) as percent
from
(
select
year,
class,
min(time_2000m) as time
from
race
where
class in ("M4x", "W4x", "M1x", "W1x")
group by
year,
class
) as A
join (
select
year,
class,
min(time_2000m) as time
from
race
where
class in ("LM4x", "LW4x", "LM1x", "LW1x")
group by
year,
class
) as B
where
A.year = B.year
and "L" || A.class = B.class
order by
A.class,
percentyear class class percent
---- ----- ----- -------
2014 M1x LM1x 1.57
2017 M1x LM1x 2.05
2015 M1x LM1x 2.15
2013 M1x LM1x 2.81
2011 M1x LM1x 3.79
2010 M1x LM1x 4.46
2015 M4x LM4x 1.51
2017 M4x LM4x 2.55
2014 M4x LM4x 3.16
2013 M4x LM4x 4.22
2010 M4x LM4x 4.58
2011 M4x LM4x 5.50
2013 W1x LW1x 1.20
2015 W1x LW1x 2.06
2017 W1x LW1x 3.66
2014 W1x LW1x 3.76
2010 W1x LW1x 3.85
2011 W1x LW1x 4.27
2014 W4x LW4x 2.48
2013 W4x LW4x 2.56
2011 W4x LW4x 2.58
2015 W4x LW4x 2.63
2017 W4x LW4x 4.58
2010 W4x LW4x 5.23
I've used this data to estimate speed differences between boat classes for a Reddit question. I am estimating the 2k times for various boat classes by averaging the fastest times found in several years. This reduces the impact of conditions but assumes crews of comparable fitness. Next I compute the factor between these times.
select class, avg(time) as time, count(*) as count from
(
select class, year, min(time_2000m) as time, descr
from race
where class in ("M8+", "M4x", "M4-", "M2x", "M2-", "M1x")
and time_2000m != ""
group by class, year
union
select class, year, min(time_2000m) as time, descr
from race
where class in ("W8+", "W4x", "W4-", "W2x", "W2-", "W1x")
and time_2000m != ""
group by class, year
)
group by class;
| class | time | count |
|---|---|---|
| M1x | 402.52 | 6 |
| M2- | 375.453333333333 | 6 |
| M2x | 367.26 | 6 |
| M4- | 348.71 | 6 |
| M4x | 340.093333333333 | 6 |
| M8+ | 326.67 | 6 |
| W1x | 444.006666666667 | 6 |
| W2- | 417.463333333333 | 6 |
| W2x | 405.135 | 6 |
| W4- | 390.058571428571 | 7 |
| W4x | 376.008333333333 | 6 |
| W8+ | 361.52 | 6 |
| M1x | M2- | M2x | M4- | M4x | M8+ | ||
|---|---|---|---|---|---|---|---|
| 2k time (s) | 402.52 | 375.45 | 367.26 | 348.71 | 340.09 | 326.67 | |
| M1x | 402.52 | 1.000 | 1.072 | 1.096 | 1.154 | 1.184 | 1.232 |
| M2- | 375.45 | 0.933 | 1.000 | 1.022 | 1.077 | 1.104 | 1.149 |
| M2x | 367.26 | 0.912 | 0.978 | 1.000 | 1.053 | 1.080 | 1.124 |
| M4- | 348.71 | 0.866 | 0.929 | 0.949 | 1.000 | 1.025 | 1.067 |
| M4x | 340.09 | 0.845 | 0.906 | 0.926 | 0.975 | 1.000 | 1.041 |
| M8+ | 326.67 | 0.812 | 0.870 | 0.889 | 0.937 | 0.961 | 1.000 |
| W1x | W2- | W2x | W4- | W4x | W8+ | ||
| 2k time (s) | 444.01 | 417.46 | 405.14 | 390.06 | 376.01 | 361.52 | |
| W1x | 444.01 | 1.000 | 1.064 | 1.096 | 1.138 | 1.181 | 1.228 |
| W2- | 417.46 | 0.940 | 1.000 | 1.030 | 1.070 | 1.110 | 1.155 |
| W2x | 405.14 | 0.912 | 0.970 | 1.000 | 1.039 | 1.077 | 1.121 |
| W4- | 390.06 | 0.878 | 0.934 | 0.963 | 1.000 | 1.037 | 1.079 |
| W4x | 376.01 | 0.847 | 0.901 | 0.928 | 0.964 | 1.000 | 1.040 |
| W8+ | 361.52 | 0.814 | 0.866 | 0.892 | 0.927 | 0.961 | 1.000 |
CREATE TABLE crew(
race INTEGER,
team TEXT,
athlete TEXT,
birthday DATE
);
CREATE TABLE race(
id INTEGER,
year INTEGER,
date DATE,
descr TEXT,
class TEXT,
number TEXT,
round TEXT,
gender TEXT,
weight TEXT,
junior TEXT,
adaptive TEXT,
coxed TEXT,
final TEXT,
time_500m REAL,
time_1000m REAL,
time_1500m REAL,
time_2000m REAL
);The data in this directory was derived from World Rowing Championship Pacing Profile Project, which in turn derived it from published race results. Compared with the original data, the data here is slighly simplified.