Analysis of team ranking

dakaufma · #1 28-03-2015, 12:19

This year's game is unusual in that there is no defense and for the most part robots work on their own in parallel to score points. As a result I figured it's reasonable (even more than usual!) to try to compute the average number of points a robot contributes to its alliance. I wrote a quick python3 script that takes as input match data (tested on the DC regional, where my former team 449 is currently playing) and outputs my ranking of teams and the number of points I think they contribute to their alliance. Apologies for the variable names, but hopefully I made up for it in with the comments.

Rankings:

1 (3419, 73.704492305515487)
2 (1731, 46.312538656273205)
3 (1418, 39.472531653896525)
4 (1895, 38.799337600450862)
5 (3490, 36.294428293019763)
6 (1599, 34.28821369481841)
7 (1885, 32.629205449774474)
8 (1033, 32.348123760832848)
9 (1421, 30.478697284154272)
10 (5338, 29.040436495747031)
11 (623, 27.745489727319374)
12 (5549, 27.121269236479719)
13 (1389, 26.60584843249497)
14 (383, 23.671855927551672)
15 (4099, 22.375266604669307)
16 (449, 21.912210928023729)
17 (116, 21.449547070425396)
18 (5243, 19.808177358212305)
19 (612, 17.344396301222865)
20 (4456, 16.61331712295609)
21 (5569, 16.37413112815706)
22 (122, 15.839607260787915)
23 (2377, 15.709065445693666)
24 (3941, 14.395635096324398)
25 (614, 13.631250573400411)
26 (2537, 11.955265914302434)
27 (686, 11.401428768949312)
28 (4541, 9.7651476520334057)
29 (4242, 8.7906915069887415)
30 (5587, 8.488917324654544)
31 (2068, 8.4789566363119739)
32 (3373, 8.3192241916003127)
33 (4821, 7.4396295708618432)
34 (2186, 6.5217325036723395)
35 (3650, 6.196980101798685)
36 (2912, 5.8563204263368132)
37 (620, 5.783804197530789)
38 (53, 5.3467216070143158)
39 (4472, 4.7981247189600955)
40 (2421, 4.7603990054157492)
41 (4464, 4.6403121971023475)
42 (2964, 4.075077317496544)
43 (1123, 3.2348248124167291)
44 (1915, -0.067152495721547467)
45 (4949, -1.8884154681926013)
46 (5520, -2.1497370184179658)
47 (3748, -2.471672298659696)
48 (611, -3.0598363988393888)

Source code:

Code:

import numpy as np

# load scores, copied from http://frc-events.usfirst.org/2015/DCWA/qualifications
f = open("score")
lines = f.readlines()
data = [l.split() for l in lines]

# these are the columns red teams, blue teams, red scores, and blue scores
# end up in when I copy/paste
redcols = [6,7,8]
bluecols = [9,10,11]
redscorecol = 12
bluescorecol = 13

# get list of teams
teams = set()
for d in data:
    for tcol in (redcols + bluecols):
        teams.add(int(d[tcol]))
teams = list(teams)
teams.sort()

# get a mapping from team number to index for efficient lookup
teamindex = {}
for i in range(len(teams)):
    teamindex[teams[i]] = i

# enter each "match", where match = [red or blue teams, score]
# (i.e. 2 "matches" per actual match played, I'm assuming that
#  the two sides are independent because there is no defense
#  and that cooperatition points are on average the same score
#  a team would have accumulated with normal socring in the same time)

# each row of match data has an entry for each team, 1 if it 
# played or 0 if it didn't
# each entry of matchscores is the score
matchdata = []
matchscores = []
for d in data:
    for (tc,tcs) in [(redcols, redscorecol), (bluecols, bluescorecol)]:
        md = np.zeros(len(teams))
        for tcol in tc:
            md[teamindex[int(d[tcol])]] = 1
        matchdata.append(md)
        matchscores.append(int(d[tcs]))

matchdata = np.array(matchdata)
matchscores = np.array(matchscores)

# compute the amount of the score contributed by each team on average
teamscores = np.linalg.lstsq(matchdata, matchscores)

# print results, sorted in order of contribution
tcs = [(t, teamscores[0][teamindex[t]]) for t in teams]
tcs.sort(key=lambda x: x[1], reverse=True)
for i in range(len(tcs)):
    print(i+1, tcs[i])

Bongle · #2 28-03-2015, 12:24

It would appear you've independently discovered what is commonly called OPR

Although it works very well this year, it can also work well in more defense-heavy years. The main breakage for OPR is exponential scoring (2007) or piece-limited scoring (2011), or when it _really_ matters how resistant you are to scoring (2009).

As a guy that once wrote a OPR scraper way back in the day, I gotta say that's a fantasticly concise implementation

dakaufma · #3 28-03-2015, 12:25

Additional comments:

- obviously there variance in team performance, so this won't be a perfect predictor
- these results list a couple teams as providing negative points on average -- this is fairly clearly not true. These teams probably contribute about 0 points on average. This actually gives a pretty good rough estimate of the precision of these numbers (error of ~3 should be expected on all numbers)
- if teams have strategies that involve working together in some way, this model does not account for that.
- if teams have strategies that interfere with each other (i.e. all 3 teams need to be fed at the feeding station to reach their full potential), this model does not account for that

dakaufma · #4 28-03-2015, 12:28

@Bongle I was actually thinking of Moneyball as I wrote this. But I actually think this game is _even better_ than baseball for such analysis because robots aren't playing dramatically different positions in these matches --> all robots have equal opportunity to score points

Bongle · #5 28-03-2015, 12:30

Quote:

- these results list a couple teams as providing negative points on average -- this is fairly clearly not true. These teams probably contribute about 0 points on average. This actually gives a pretty good rough estimate of the precision of these numbers (error of ~3 should be expected on all numbers)

That's not entirely true - the way the math works out, you end up solving for "the number of points added by that team being in an alliance". If a team tends to take fouls, gets in the way, blocks visibility, or breaks their alliance partners, they may end up with a negative value.

Similarly, it is possible for a team to have a positive value without ever scoring a single piece: if the team facilitates scoring in some way (good strategy, supplying parts, bringing a ramp, etc), they may still have a positive effect on the alliance's score.

Your other points about limitations are correct.

Ether · #6 28-03-2015, 14:02

Quote:

Originally Posted by dakaufma

I wrote a quick python3 script that takes as input match data ... and outputs my ranking of teams and the number of points I think they contribute to their alliance.

Would you please run a short experiment?

Attached is a ZIP file containing Qual Match data for all 67 events in 2015 weeks 1 through 4, in the column format described in your code (I included both tab-delimited and comma-delimited).

Please run your script on that data and tell us how long it takes the script to do the computation.

Ether · #7 28-03-2015, 14:15

Quote:

Originally Posted by Ether

Would you please run a short experiment?

Attached is a ZIP file containing Qual Match data for all 67 events in 2015 weeks 1 through 4, in the column format described in your code (I included both tab-delimited and comma-delimited).

Please run your script on that data and tell us how long it takes the script to do the computation.

Note: all the data is in one big file; you only need run the script once on that file (not 67 separate runs)

dakaufma · #8 28-03-2015, 14:52

It runs out of memory after a few seconds computing least squares. I guess my script was too quick and dirty to handle that much data

Ok, closed out of my memory-hog browser and reran it. It finished in 68 seconds. It's single threaded --> only using 1 core. I don't know the exact specs but I'm using an old laptop. If you want it to run faster you could get a reasonable estimate running the script for each regional and averaging the results for teams that competed in multiple regionals. My script basically ran instantly on the DC regional alone, it just didn't scale well.

Results:

1 (1114, 119.53710628381191)
2 (2056, 107.52392526361433)
3 (1730, 98.937520154912633)
4 (2481, 85.411141027422389)
5 (1678, 83.604692998775761)
6 (987, 81.614933990522147)
7 (118, 80.969024034928296)
8 (4488, 80.539400149979258)
9 (254, 79.885031034931174)
10 (148, 79.432991922526838)
11 (5406, 78.607729049050903)
12 (1519, 78.606114913994432)
13 (1619, 77.525461715196158)
14 (1658, 72.769643357190205)
15 (3130, 71.271318091324432)
16 (1671, 70.02262062115436)
17 (2338, 68.377599976884255)
18 (1280, 68.31086262054076)
19 (3683, 68.046518730572274)
20 (314, 67.063058451290729)
21 (4143, 66.197146406242695)
22 (2085, 65.826088138212199)
23 (1756, 65.300090499752486)
24 (225, 64.935706448058212)
25 (1538, 64.559840785724475)
26 (330, 64.541264057923442)
27 (2852, 64.483030815000632)
28 (1986, 62.974425411332938)
29 (2451, 62.637597604073804)
30 (2974, 60.564349115146001)
31 (1023, 60.560211511217332)
32 (1983, 59.916674128430934)
33 (744, 59.594728708265407)
34 (1657, 59.279400181300687)
35 (67, 58.964359968912603)
36 (2122, 58.817777660610751)
37 (2342, 57.489327516287716)
38 (1640, 57.464812178955)
39 (2826, 56.916928984985773)
40 (234, 56.858881704686439)
41 (3339, 56.533409767618949)
42 (4655, 56.39313720643888)
43 (3230, 56.122255030023631)
44 (1574, 55.625450146975965)
45 (624, 55.340768974856552)
46 (2077, 55.213700500454443)
47 (525, 55.123176093827624)
48 (2783, 54.867531033295869)
49 (303, 54.677252325474406)
50 (2512, 54.663228166283453)
51 (1156, 53.95917064433668)
52 (701, 53.489926552157726)
53 (135, 53.310632416341505)
54 (3824, 52.981920713390842)
55 (4678, 52.90258841628895)
56 (2137, 52.82827564039772)
57 (1806, 52.545135953520244)
58 (4201, 52.100455606412289)
59 (4967, 51.976661185189229)
60 (58, 51.708557689346009)
61 (1569, 50.999721751403754)
62 (2587, 50.879055731499093)
63 (3663, 50.352150843306362)
64 (3360, 50.32364073573774)
65 (70, 50.212423608681007)
66 (456, 49.514397347218271)
67 (226, 49.421619066877071)
68 (3419, 49.291886530691805)
69 (4613, 49.182337568239383)
70 (2383, 48.952668799007341)
71 (4451, 48.461565771037783)
72 (4039, 48.428887067643302)
73 (60, 48.304027027456911)
74 (3452, 48.237009235381947)
75 (694, 48.146194119499853)
76 (2643, 47.617699872486028)
77 (85, 47.524384916070105)
78 (1403, 47.469950277471035)
79 (494, 47.374147160288466)
80 (1918, 47.291935686802972)
81 (287, 47.257974979802917)
82 (2386, 46.953430407180903)
83 (203, 46.861955923046182)
84 (1024, 46.697459369030227)
85 (876, 46.137245094936418)
86 (3646, 46.13325578237896)
87 (340, 46.084590782880028)
88 (3132, 45.713812619738299)
89 (4564, 45.704005687502573)
90 (71, 45.592449645849975)
91 (2337, 45.302005071186898)
92 (3200, 45.067571065424303)
93 (379, 44.960948960140477)
94 (2054, 44.735136796993629)
95 (1208, 44.698482053595882)
96 (78, 44.46540330346042)
97 (2767, 44.397595621836118)
98 (1676, 44.241945432579165)
99 (2231, 44.074739609953014)
100 (1876, 44.059135373758906)
101 (5188, 44.006924999206518)
102 (33, 43.991463573919681)
103 (662, 43.937410242633831)
104 (107, 43.844215107033079)
105 (2883, 43.838433292634448)
106 (126, 43.680710097846031)
107 (4522, 43.658797513513953)
108 (125, 43.554274627035511)
109 (4539, 43.541714417941485)
110 (2457, 43.313197516131233)
111 (1768, 43.202978983685014)
112 (2062, 42.91184275827608)
113 (3996, 42.878462653349217)
114 (1218, 42.221423588184592)
115 (4947, 42.21385679919382)
116 (4028, 42.184039856147834)
117 (4003, 42.058582095894693)
118 (230, 41.974917059019731)
119 (610, 41.906402824077851)
120 (2607, 41.670540732672599)
121 (4917, 41.384462943129392)
122 (3688, 41.147751248112812)
123 (3930, 41.033422854244797)
124 (1493, 40.986924808173157)
125 (4500, 40.741418876306227)
126 (4118, 40.695131685614619)
127 (971, 40.523141542947506)
128 (3238, 40.466262103026601)
129 (359, 40.422448697174524)
130 (61, 40.330053037398599)
131 (3418, 40.150604856512359)
132 (48, 40.137305510265087)
133 (2996, 40.028508239671567)
134 (2619, 39.968708976433774)
135 (5030, 39.877431370188553)
136 (263, 39.839439286180657)
137 (4048, 39.825826430840849)
138 (1296, 39.808174436676225)
139 (56, 39.662074105094518)
140 (4049, 39.655310597148734)
141 (4946, 39.601027916048579)
142 (3604, 39.539475208943301)
143 (348, 39.481859806964366)
144 (1318, 39.370452934396013)
145 (3467, 39.295360485121932)
146 (4911, 39.224358414364175)
147 (3616, 39.110279455887301)
148 (5618, 39.086526813133673)
149 (2635, 38.980266823053611)
150 (2930, 38.840601375514616)
151 (3374, 38.81788764505211)
152 (4256, 38.784719563512631)
153 (973, 38.463844062433054)
154 (51, 38.334314781676767)
155 (384, 38.201138768628411)
156 (2590, 38.147515826742477)
157 (4096, 38.116308218237869)
158 (4623, 38.092545615566564)
159 (4952, 37.86114845595975)
160 (133, 37.828067879761136)
161 (1718, 37.787600674391747)
162 (2474, 37.692730385543939)
163 (3743, 37.59312775479782)
164 (1816, 37.542280174166791)
165 (2410, 37.496877654564656)
166 (1501, 37.104342080915757)
167 (3620, 37.037606938363133)
168 (5122, 36.982541576210828)
169 (4481, 36.865262298598083)
170 (3937, 36.841636721001414)
171 (1647, 36.751313815118657)
172 (3284, 36.74635714111696)
173 (1310, 36.716915159692995)
174 (3711, 36.708942403153074)
175 (2168, 36.657044244067841)
176 (219, 36.622331677278126)
177 (4618, 36.592685886997145)
178 (4009, 36.541388801745121)
179 (179, 36.46999930280974)
180 (192, 36.428357257530422)
181 (1511, 36.412379877666389)
182 (2468, 36.411558461182182)
183 (1466, 36.410653763267597)
184 (176, 36.341368086411364)
185 (3954, 36.106465544323413)
186 (236, 36.030241466107022)
187 (1065, 35.866091693146807)
188 (217, 35.739433335936802)
189 (293, 35.714822450042767)
190 (3171, 35.700953362598391)
191 (4450, 35.675245209103132)
192 (3478, 35.497911882995631)
193 (1025, 35.33059600491984)
194 (2067, 35.302384982157086)
195 (858, 35.220534346307758)
196 (20, 35.213858881906404)
197 (3787, 35.200316831586413)
198 (3245, 35.154820267538746)
199 (3947, 34.936457120925624)
200 (5124, 34.900427199797058)
201 (1690, 34.843920361381095)
202 (2605, 34.842650608275285)
203 (469, 34.826502670309935)
204 (88, 34.781860065335088)
205 (4471, 34.776523328229906)
206 (3055, 34.772009951076569)
207 (1629, 34.734579749225233)
208 (492, 34.62041348642861)
209 (4859, 34.584359681765029)
210 (2470, 34.584346687341977)
211 (188, 34.462145048031729)
212 (5155, 34.435692705316441)
213 (2522, 34.35816391473486)
214 (34, 34.173866945917425)
215 (5415, 34.146785509913705)
216 (1502, 34.067087880802461)
217 (4001, 34.064597422588839)
218 (4055, 33.916169496312733)
219 (1094, 33.883434363608139)
220 (1523, 33.871609512708311)
221 (5479, 33.849391237999257)
222 (3528, 33.828080185259267)
223 (5069, 33.77734334023711)
224 (3574, 33.690314713091702)
225 (1572, 33.650008671190953)
226 (2170, 33.575593261620348)
227 (3015, 33.547013037090167)
228 (3959, 33.523300871264311)
229 (3044, 33.406171382153723)
230 (904, 33.342751678172135)
231 (846, 33.327700541596251)
232 (4377, 33.305432679592052)
233 (670, 33.259397175471463)
234 (3309, 33.247368517097016)
235 (5753, 33.236142903316946)
236 (2169, 33.094046510981954)
237 (5492, 33.046067910839866)
238 (4061, 33.01891123496646)
239 (280, 32.985235881445348)
240 (1937, 32.945294053227798)
241 (4330, 32.895140642192821)
242 (2959, 32.807809147273616)
243 (4381, 32.793946338233482)
244 (245, 32.711707797251968)
245 (292, 32.579249817782227)
246 (4914, 32.53912196307197)
247 (246, 32.470985984479007)
248 (2471, 32.404626971136231)
249 (4646, 32.384273327934203)
250 (1225, 32.374217436032403)
251 (1261, 32.345344589914419)
252 (3618, 32.319420811573949)
253 (706, 32.087559612208366)
254 (2489, 32.080162945238371)
255 (5674, 32.068028144346975)
256 (365, 32.063866801911502)
257 (2550, 31.968333087158392)
258 (2907, 31.963996692538135)
259 (4543, 31.937642440156992)
260 (2344, 31.883372165807916)
261 (5172, 31.746782970946477)
262 (3968, 31.738032978366853)
263 (4004, 31.660944083506916)
264 (5603, 31.656178443794023)
265 (3547, 31.624098353856535)
266 (27, 31.615954801848815)
267 (5511, 31.59945800797674)
268 (4485, 31.588001091120738)
269 (1595, 31.558596969881869)
270 (3539, 31.556723877535198)
271 (2478, 31.435819566154908)
272 (3039, 31.41599321476928)
273 (447, 31.408100879172181)
274 (3955, 31.397208810781596)
275 (2040, 31.396818339564696)
276 (4384, 31.36827170756532)
277 (5112, 31.338720289318339)
278 (1712, 31.225301368796138)
279 (1763, 31.219614687869981)
280 (1319, 31.027832060889921)
281 (5053, 31.026096768004098)
282 (316, 31.004945681413208)
283 (195, 31.002772387355328)
284 (138, 30.978073568382566)
285 (5502, 30.96655526350947)
286 (1477, 30.854716412418391)
287 (5776, 30.833279403898562)
288 (45, 30.771497026266104)
289 (3588, 30.730249883028147)
290 (222, 30.661345061396982)
291 (2081, 30.585445374235022)
292 (5554, 30.579978307945261)
293 (1720, 30.573435485801873)
294 (4624, 30.501204083289633)
295 (4498, 30.421878762510477)
296 (2526, 30.412593900599504)
297 (5758, 30.403677283741686)
298 (3003, 30.289741529784614)
299 (383, 30.274427792634622)
300 (1723, 30.218861857075929)
301 (811, 30.192776246588522)
302 (342, 30.168485860212456)
303 (4189, 30.13965943188408)
304 (171, 30.111080057728859)
305 (1885, 30.08157855417863)
306 (3501, 30.056119432123456)
307 (885, 30.042265795522237)
308 (4325, 29.978999286032156)
309 (3612, 29.972457662016737)
310 (433, 29.965135411743013)
311 (343, 29.815721870600417)
312 (4550, 29.783543177508189)
313 (5735, 29.760366282137795)
314 (3451, 29.623168415939425)
315 (193, 29.597381522938797)
316 (1665, 29.477226112588852)
317 (5038, 29.371603785511478)
318 (5013, 29.367568082295421)
319 (1577, 29.351057855544511)
320 (1522, 29.329602508326648)
321 (3623, 29.319629407542678)
322 (3944, 29.165672846015873)
323 (5254, 29.152950147461201)
324 (3570, 29.146301900278111)
325 (308, 29.108891367260316)
326 (2867, 29.07047844203802)
327 (4905, 29.050359447749955)
328 (4103, 29.008081199277097)
329 (93, 29.002439064178919)
330 (269, 28.990050531142835)
331 (3357, 28.987813199246801)
332 (4906, 28.987217737312413)
333 (1492, 28.931717575968353)
334 (1735, 28.843015541771116)
335 (5442, 28.724169408312502)
336 (3336, 28.712610544003628)
337 (5413, 28.696880079236056)
338 (4364, 28.611574275852828)
339 (1775, 28.584899036665753)
340 (3065, 28.525796636906136)
341 (2612, 28.469954244499142)
342 (2052, 28.430475381231936)
343 (157, 28.411865437907171)
344 (4586, 28.402948306541667)
345 (418, 28.385761801791819)
346 (1706, 28.370790285515668)
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1900 (4166, 2.9519303789271882)
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1911 (3536, 2.809543421079149)
1912 (4745, 2.7648953954673474)
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1914 (1626, 2.7350489113513881)
1915 (5454, 2.726327943186738)
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1917 (4657, 2.7241621721413471)
1918 (3530, 2.7226127210775388)
1919 (5662, 2.6952116893627291)
1920 (5025, 2.6828801168430694)
1921 (599, 2.6796512550051825)
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1927 (1481, 2.5266522809240675)
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1929 (5019, 2.4399550296330146)
1930 (468, 2.4249445253495452)
1931 (4316, 2.4154190312528234)
1932 (832, 2.3896951461772367)
1933 (4739, 2.3758433078076675)
1934 (271, 2.3528768331460421)
1935 (4053, 2.3459907616074194)
1936 (4335, 2.3193747406024383)
1937 (4378, 2.2885253893875186)
1938 (1716, 2.2460283070541793)
1939 (2532, 2.2245476691802972)
1940 (177, 2.2197710483159767)
1941 (4649, 2.2164849427276887)
1942 (1786, 2.1785390447517301)
1943 (5329, 2.1784256537000424)
1944 (2106, 2.1731576587137034)
1945 (1899, 2.155975627210895)
1946 (4291, 2.1382759946007894)
1947 (3297, 2.1373810849653285)
1948 (514, 2.1104002392788916)
1949 (4776, 2.10752549999936)
1950 (3160, 2.0919319806069883)
1951 (4035, 2.0581370727974173)
1952 (5143, 2.0570142016493689)
1953 (2543, 2.0549796683460921)
1954 (5553, 2.0312558322054972)
1955 (3029, 2.0012105678446233)
1956 (888, 1.9733412866993714)
1957 (4818, 1.960540935785164)
1958 (3997, 1.9046296450651501)
1959 (4296, 1.875614152445225)
1960 (886, 1.8366737309182568)
1961 (3617, 1.7869275164567551)
1962 (3712, 1.786743748548357)
1963 (5003, 1.7821350761119084)
1964 (136, 1.7806593125081491)
1965 (2001, 1.7353097101149333)
1966 (4265, 1.7119372233648948)
1967 (4506, 1.6398028727312761)
1968 (5759, 1.6389429309843759)
1969 (499, 1.6296072797872518)
1970 (2634, 1.6184201644462242)
1971 (5273, 1.615391128682639)
1972 (5240, 1.5866046576372799)
1973 (3932, 1.5520322073853277)
1974 (5486, 1.550140000933057)
1975 (4746, 1.530019884626147)
1976 (3676, 1.5275755900622188)
1977 (5641, 1.5250501189603032)
1978 (714, 1.5161596439605072)
1979 (5560, 1.5047139451403773)
1980 (5464, 1.4714626645031283)
1981 (5207, 1.4552401302084477)
1982 (2586, 1.4389365583288569)
1983 (4901, 1.4331484517071462)
1984 (4854, 1.4266156938314181)
1985 (5135, 1.4144366040051661)
1986 (5250, 1.4138439895597152)
1987 (3006, 1.4125927448594862)
1988 (4768, 1.3628137921052716)
1989 (5421, 1.3297700015758236)
1990 (1994, 1.3283402531410557)
1991 (4065, 1.3129637218326178)
1992 (5080, 1.3037091762849613)
1993 (2830, 1.2893982597573719)
1994 (4516, 1.2278007778775937)
1995 (5639, 1.1859457940752018)
1996 (4470, 1.1668213179193436)
1997 (5670, 1.1023982856714671)
1998 (1965, 1.090569897853273)
1999 (2048, 1.0647725219671678)
2000 (224, 1.0592017519849648)
2001 (2491, 1.0327813882156693)
2002 (5622, 1.0053550304949574)
2003 (5092, 0.9761417379400168)
2004 (5583, 0.96865016883601551)
2005 (4614, 0.94890534621633971)
2006 (4696, 0.93133356181886273)
2007 (1571, 0.92306225375204387)
2008 (3345, 0.92272367815652412)
2009 (421, 0.88358742145534686)
2010 (5648, 0.87451545885190352)
2011 (4757, 0.80282520580686545)
2012 (5561, 0.77400478010715901)
2013 (2360, 0.75508258417021423)
2014 (5467, 0.75352458117194587)
2015 (4090, 0.74472559875291322)
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2017 (4244, 0.73397829641860635)
2018 (4353, 0.63560642684621382)
2019 (4978, 0.60542778478719317)
2020 (3781, 0.59504797747290039)
2021 (4841, 0.59004411619813801)
2022 (3255, 0.5810465132294006)
2023 (5310, 0.57673949159014182)
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2025 (5136, 0.52102984923353091)
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2027 (3370, 0.46951477407945041)
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2029 (4, 0.4306228979039588)
2030 (3302, 0.40207695189753068)
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2034 (2029, 0.34372910656585376)
2035 (2399, 0.33640170027807154)
2036 (3131, 0.29971469544445972)
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2039 (5598, 0.23411363482723413)
2040 (5519, 0.2039588059208457)
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2042 (3026, 0.17714070309077301)
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2045 (1984, 0.10877219279701666)
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2243 (5130, -12.327070764611266)
2244 (3975, -12.389703622210016)
2245 (3981, -12.429025986961294)
2246 (2973, -12.539331608387576)
2247 (4217, -14.394933572740818)
2248 (1138, -14.579713512391079)
2249 (256, -15.438268674725213)
2250 (2613, -16.231781671266916)
67.87user 0.10system 1:08.05elapsed 99%CPU (0avgtext+0avgdata 399012maxresident)k
0inputs+0outputs (0major+53269minor)pagefaults 0swaps

MikLast · #9 28-03-2015, 15:12

so is this just if all teams were at the DC regional based on their score?

It would put us in the top 30% of teams...

Ether · #10 28-03-2015, 16:01

Quote:

Originally Posted by dakaufma

It finished in 68 seconds.

Try this instead:

1) compute N = A^T∙A

2) compute d = A^T∙b

3) compute teamscores = scipy.linalg.cho_solve(scipy.linalg.cho_factor(N), d)

... where A is np.array(matchdata) and b is np.array(matchscores)

The computation time should be reduced from 68 seconds to about 2 seconds or less.

dakaufma · #11 28-03-2015, 16:09

Quote:

Originally Posted by MikLast

so is this just if all teams were at the DC regional based on their score?

No, not quite, this is a slightly different computation. I'm trying to answer the question "how much value does each robot contribute to its alliance?"

In most (qualification) matches robots operate reasonably independently from one another. This makes my question a lot easier to answer -- if robots don't influence each other's performance, then each robot should contribute (on average) the same score to any alliance it plays with. So I've got one variable per team -- how many points it contributes to its alliance -- and I'm trying to solve for the set of values for these variables that comes closest to predicting the actual match scores.

The listing above is my estimate of how many points each robot contributes to its alliance (given no interaction with other robots)

MikLast · #12 28-03-2015, 16:13

Quote:

Originally Posted by dakaufma

No, not quite, this is a slightly different computation. I'm trying to answer the question "how much value does each robot contribute to its alliance?"

In most (qualification) matches robots operate reasonably independently from one another. This makes my question a lot easier to answer -- if robots don't influence each other's performance, then each robot should contribute (on average) the same score to any alliance it plays with. So I've got one variable per team -- how many points it contributes to its alliance -- and I'm trying to solve for the set of values for these variables that comes closest to predicting the actual match scores.

The listing above is my estimate of how many points each robot contributes to its alliance (given no interaction with other robots)

I understand that, then this just means that the teams at the DC regional were skewed, or something else?

Quote:

My script basically ran instantly on the DC regional alone, it just didn't scale well.

dakaufma · #13 28-03-2015, 16:22

I haven't done any in-depth analysis, but DC doesn't look too far out of line from the set of all regionals.

@MikLast I think I misinterpreted what you were saying. This would be a reasonable estimate of how well teams would do if each team played this game without an alliance. So congrats if you team is in the top 30%, you have a good robot

MikLast · #14 28-03-2015, 16:26

Quote:

Originally Posted by dakaufma

@MikLast I think I misinterpreted what you were saying. This would be a reasonable estimate of how well teams would do if each team played this game without an alliance. So congrats if you team is in the top 30%, you have a good robot

Ah, i misunderstood you also then. Thank you for clarifying, and i cant wait to show my team this next week.

Ether · #15 28-03-2015, 20:42

Quote:

Originally Posted by Ether

1) compute N = A^T∙A

2) compute d = A^T∙b

3) compute teamscores = scipy.linalg.cho_solve(scipy.linalg.cho_factor(N), d)

... where A is np.array(matchdata) and b is np.array(matchscores)

The computation time should be reduced from 68 seconds to about 2 seconds or less.

I'd run this test myself but I have Python2.7.5 installed and your Python3 code crashes when I try to run it. Not being very fluent in Python, I'm not in a good position to try to port it.

Based on some testing I did here, I'm fairly confident that your computation time can be dramatically reduced by making the small changes shown above.

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