DoNe does data analysis

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timakro
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DoNe does data analysis

Post by timakro »

This topic is a series where I'll frequently add new analyses of the ddnet stats data, that is released daily. I'll include the code snippets used to calculate the results. I'll also explain them roughly, because I use python they should be easy to understand for you. Feel free to suggest new analyses below this topic but please make sure that they're not already done. If you're interested in the data, I'm using the ddnet stats in the csv format, you can find the download link in this thread.

Already present data analyses
To make this topic complete I'll give links to sites where people already did some data analyses with the ddnet stats. Ofcourse the Statistics & Charts page has some analyses of the ddnet stats but using the internal SQL database. Also deen did data analysis in The History of DDNet thread. Please check these out before suggesting new analyses.

Python module
I'm using a self written python module that downloads the ddnet stats and converts some of the data to python objects which is ridiculous slow. Though it's pretty comfortable to work with python objects in the analysis later. The module can be found at github. I'm not gonna write the first lines of every script in this thread over and over. Just rember that these variables exist in every script.

Code: Select all

import ddnet_stats

maps = ddnet_stats.get_maps()
finishes = ddnet_stats.get_finishes()
teamfinishes = ddnet_stats.get_teamfinishes()
These variables are lists filled with dictonaries. Every dictionary in a list has the same keys. Here's a list of the keys and an explanation of them if it's not obious what there values mean.
  • maps
    • mapper
    • name -> Mapname
    • mappers -> List of all mappers
    • server -> e.g. "Brutal"
    • points
    • stars
    • release -> datetime.datetime object, can be None since release times weren't recorded at the start of DDNet
  • finishes
    • date -> datetime.datetime object when the map was finished
    • map
    • time -> datetime.timedelta object of the finish time
    • server -> e.g. "GER", can be None since servers weren't saved earilier
    • player
  • teamfinishes
    The same keys as finishes except for server is not included and the team key is added:
    • team -> team id for the teamfinish, e.g. "c8ceea1d-f48e-11"
Another important thing to mention is that every teamfinish is included in the finishes list. The teamfinishes list is just to concatenate the finishes.

Understanding the data
First off I have some questions about the data. Let's get right into it by calculating some of the information you can see ingame using the ddnet stats. We will compare it to the ingame results to see if we understand the data right.

How does DDNet calculate the number of mapfinishes on a map?
As an example I choose the map Kobra to calculate the number of finishes. My first attempt was to calculate the mapfinishes by the number of finishes plus the number of teamfinishes with a unique team id.

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mapname = "Kobra"
mapfinishes = 0
for f in finishes:
    if f["map"] == mapname:
        mapfinishes += 1
teams = []
for f in teamfinishes:
    if f["map"] == mapname and f["team"] not in teams:
        mapfinishes += 1
        teams.append(f["team"])
print "There are", mapfinishes, "finishes on", mapname
First I go over all the finishes using a for loop. If the finish was made on Kobra I increase the number of mapfinishes. In the second loop I go over the teamfinishes. Ofcourse I check again if the mapname matches and if thats the case I make sure to count a team only once by using a list which holds the already counted teams.
My script says There are 16187 finishes on Kobra, which turns out to be wrong since it says ingame *** "Kobra" by Zerodin on Novice (★★★★✰, 4 points, 15202 finishes by 6366 tees, released 1 year and 6 months ago). So what went wrong here? I couldn't figure it out and asked deen for help he told me that the teamfinishes are already included in the finishes so we can easily fix it by removing the second loop. Additionally we'll add a list which holds all the different players that finished Kobra, so we can count them later.

Code: Select all

players = []The enumerate function simply makes it possible to get the number of the item in the list.
for i, f in enumerate(finishes):
    if f["map"] == mapname:
        mapfinishes += 1
        if f["player"] not in players:
            players.append(f["player"])
print "There are", mapfinishes, "finishes by", len(players), "players on", mapname
There are 15200 finishes by 6365 players on Kobra, looks right! Since the data is only released daily at 5:30 am, a small difference is fine.

Calculating a players points

Code: Select all

player = "Juandissimo"
points = 0
added_maps = []
for f in finishes:
    if f["player"] == player and f["map"] not in added_maps:
        for m in maps:
            if m["name"] == f["map"]:
                points += m["points"]
                break
        added_maps.append(f["map"])
print player, "has", points, "points"
First we go over every finish. If the finish is by Juandissimo and we haven't already counted the map we add the maps points to Juandissimo's points and add the map to the list of already counted maps.
My script says Juandissimo has 6221 points, which is totally right! *** 19. Juandissimo Points: 6221, requested by DoNe.

Are team ids unique for every finish?
So my question is if they are really unique for one finish or maybe for a combination of players or anything like that.

Code: Select all

for i, a in enumerate(teamfinishes):
    if i%100 == 0:
        print i, "/", len(teamfinishes)
    for b in teamfinishes:
        if a["team"] == b["team"] and a["time"] != b["time"]:
            print "Not unique!"
            sys.exit()
print "Unique!"
The enumerate function simply makes it possible to get the number of the item in the list. Since this takes ages to calculate I added a system that tells me how many calculations are done so far. If the number of the item in the list we are currently at devided by 100 is an integer we print how far we are with the calculations. We assume that if there are two teamfinishes which team ids are equal but there finish times aren't equal the team ids are not unique for every finish in general. sys.exit just terminates the whole program so print "Unique!" will not be executed anymore. Happy scrolling! :P

Code: Select all

Are team ids unique for one finish?
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Unique!
Is finishing alone in a team considered as a teamfinish?
We just see if we can find a team id that occures only once in the data. If we do so we can surely say that it is considered as a teamfinish. Otherwise we can say that it is probably not considered as a teamfinish. "Probably", since it could be possible that just nobody finished ever alone in a team.

Code: Select all

alone = {}
for t in teamfinishes:
    team = t["team"]
    if team not in alone:
        alone[team] = True
    else:
        alone[team] = False
print "That finishing alone in a team is saved as a teamfinish is", "right" if True in alone.values() else "very likely wrong"
There we go That finishing alone in a team is saved as a teamfinish is right, thats what i expected.

When quick tournament maps are "released"?
For this question I went over all the quick tournament maps on the Tournaments page and collected the names of the maps and the corresponding time of the tournament start. This took me for ages but here we've got the list. I set the timezones to CET since thats the timezone the ddnet stats are in and we want to compare the times.

Code: Select all

tz = dateutil.tz.gettz("CET")
tournas = {
    "Level": dt.time(hour=20, tzinfo=tz),
    "Threesome": dt.time(hour=22, tzinfo=tz),
    "Kobra": dt.time(hour=20, tzinfo=tz),
    "Valley 2": dt.time(hour=20, tzinfo=tz),
    "Thor": dt.time(hour=17, tzinfo=tz),
    "Green Agony": dt.time(hour=20, tzinfo=tz),
    "Ice Cave 1": dt.time(hour=20, tzinfo=tz),
    "SkyCave": dt.time(hour=22, tzinfo=tz),
    "Kobra 2": dt.time(hour=19, tzinfo=tz),
    "Space Climb": dt.time(hour=20, tzinfo=tz),
    "Into the Wild": dt.time(hour=19, tzinfo=tz),
    "Ice Cave 2": dt.time(hour=19, tzinfo=tz),
    "Kindergarten": dt.time(hour=19, tzinfo=tz),
    "ROCKET-BELT": dt.time(hour=19, tzinfo=tz),
    "Simple World": dt.time(hour=20, tzinfo=tz),
    "Naufrage 2": dt.time(hour=20, tzinfo=tz),
    "Planet Mars": dt.time(hour=20, tzinfo=tz),
    "Falling Down": dt.time(hour=20, tzinfo=tz),
    "Simple World 2": dt.time(hour=20, tzinfo=tz),
    "Chill Let's Climb 2": dt.time(hour=20, tzinfo=tz),
    "Planet Venus": dt.time(hour=20, tzinfo=tz),
    "Inception": dt.time(hour=20, tzinfo=tz),
    "Rivendell": dt.time(hour=20, tzinfo=tz),
    "Eternal": dt.time(hour=20, tzinfo=tz),
    "Kobra 3": dt.time(hour=20, tzinfo=tz),
    "Fried": dt.time(hour=20, tzinfo=tz),
    "SunDay 2": dt.time(hour=20, tzinfo=tz),
    "Rollercoaster": dt.time(hour=20, tzinfo=tz),
    "Skychase": dt.time(hour=20, tzinfo=tz),
    "Spooky": dt.time(hour=20, tzinfo=tz),
    "Fallen Angel": dt.time(hour=20, tzinfo=tz),
    "Nirvana": dt.time(hour=20, tzinfo=tz),
    "TomorrowLand": dt.time(hour=20, tzinfo=tz),
    "Jvice": dt.time(hour=20, tzinfo=tz),
    "Aim 8.0": dt.time(hour=20, tzinfo=tz),
    "UpNDown": dt.time(hour=20, tzinfo=tz),
    "DarkSpy": dt.time(hour=20, tzinfo=tz),
    "Red Avenue": dt.time(hour=20, tzinfo=tz),
    "Desert Wolf": dt.time(hour=20, tzinfo=tz),
    "Eternal 2": dt.time(hour=20, tzinfo=tz),
    "Planet Jupiter": dt.time(hour=20, tzinfo=tz),
    "Everdeen": dt.time(hour=20, tzinfo=tz),
    "KeepOut": dt.time(hour=20, tzinfo=tz),
    "Optimum": dt.time(hour=20, tzinfo=tz)
}
So let's go over the tournament maps and replace the tournament start times with the differences to the release times.

Code: Select all

for mapname, start in tournas.items():
    for m in maps:
        if m["name"] == mapname:
            startday = dt.datetime.combine(m["release"].date(), start)
            delta = m["release"] - startday
            tournas[mapname] = delta
            break
    else:
        print mapname, "not found"
        sys.exit()
We use the datetime.datetime.combine function to combine the map release date with the tournament start time. After that we substract the map release datetime by the tournament start datetime. Let's sort the tournament maps now by their time differences and print the results.

Code: Select all

tournas = sorted(tournas.items(), key=lambda x: x[1])
for mapname, delta in tournas:
    if delta < dt.timedelta(0):
        print mapname, "was released", delta*-1, "before the tournament started"
    elif delta > dt.timedelta(0):
        print mapname, "was released", delta, "after the tournament started"
    else:
        print mapname, "was released exactly when the tournament started"
Note that we multiply the time differences at the first print by -1 since it is negative (because the map was released before the tournament start) and we want a positive value (since we want to say how long it was released before). The script produces the following output.

Code: Select all

SkyCave was released 2:01:00 before the tournament started
Inception was released 0:37:00 before the tournament started
Rivendell was released 0:33:00 before the tournament started
Falling Down was released 0:20:00 before the tournament started
Naufrage 2 was released 0:18:00 before the tournament started
Into the Wild was released 0:18:00 before the tournament started
Kobra was released 0:17:00 before the tournament started
Valley 2 was released 0:17:00 before the tournament started
Chill Let's Climb 2 was released 0:16:00 before the tournament started
Kobra 3 was released 0:16:00 before the tournament started
Planet Venus was released 0:16:00 before the tournament started
Space Climb was released 0:14:00 before the tournament started
Fried was released 0:13:00 before the tournament started
Kindergarten was released 0:13:00 before the tournament started
Eternal was released 0:13:00 before the tournament started
Green Agony was released 0:12:00 before the tournament started
Thor was released 0:12:00 before the tournament started
Planet Mars was released 0:07:00 before the tournament started
Skychase was released 0:06:00 before the tournament started
Simple World was released 0:06:00 before the tournament started
Desert Wolf was released 0:05:00 before the tournament started
Spooky was released 0:05:00 before the tournament started
Everdeen was released 0:05:00 before the tournament started
DarkSpy was released 0:05:00 before the tournament started
KeepOut was released 0:04:00 before the tournament started
Simple World 2 was released 0:04:00 before the tournament started
Kobra 2 was released 0:04:00 before the tournament started
Planet Jupiter was released 0:04:00 before the tournament started
Eternal 2 was released 0:04:00 before the tournament started
Ice Cave 1 was released 0:04:00 before the tournament started
Nirvana was released 0:03:00 before the tournament started
ROCKET-BELT was released 0:03:00 before the tournament started
Ice Cave 2 was released 0:03:00 before the tournament started
Aim 8.0 was released 0:02:00 before the tournament started
Jvice was released 0:02:00 before the tournament started
Fallen Angel was released 0:02:00 before the tournament started
Red Avenue was released 0:02:00 before the tournament started
SunDay 2 was released 0:02:00 before the tournament started
TomorrowLand was released 0:02:00 before the tournament started
UpNDown was released exactly when the tournament started
Rollercoaster was released 0:01:00 after the tournament started
Optimum was released 0:01:00 after the tournament started
Level was released 0:22:00 after the tournament started
Threesome was released 0:42:00 after the tournament started
We can see that almost every map was released before the tournament started. I also count UpNDown to the "normal" tournaments. Level and Threesome were the maps of the first quick tournament on DDNet, so I guess that another system was used for running tournaments back then, where the map gets released after the tournament ended. I remember starting problems on Rollercoaster, so thats probably why its released one minute later. I guess something similar must have happend at the Optimum tournament. Another extreme is SkyCave but I can't find a reason why its released so early. By taking another look at the tournament site of SkyCave I found this This tournament was played on 2014-03-30 from 22:00 to 22:00 CEST. If that was true, this would have been a really short tournament. :D Probably the tournament started at 20:00, so the time difference would have been only a minute.

For now I have no more questions about the data itself, let's get to the interesting part and start with the first data analysis!

Data analyses
I'll try to release the analyses in an order, so that they get more complex over time. The working code for every data analysis can be downloaded on github.

001 - What is the most common team on DDNet?
We basically count the finishes by a team of players and sort the teams by their finishes. We start by concatenating the players with the same team ids.

Code: Select all

teamids = {}
for t in teamfinishes:
    tid = t["team"]
    if tid not in teamids:
        teamids[tid] = [t["player"]]
    else:
        teamids[tid].append(t["player"])
Now we go over the teams of players and count how many finishes they have.

Code: Select all

teams = {}
for t in teamids.values():
    t = frozenset(t)
    if t not in teams:
        teams[t] = 1
    else:
        teams[t] += 1
And last but not least we sort the teams by their number of finishes, choose the first 50 teams and print the results.

Code: Select all

teams = sorted(teams.items(), key=lambda x: x[1], reverse=True)
for rank, (team, finishes)  in enumerate(teams[:50]):
    print "rank", rank+1, "team", ", ".join(team), "finishes", finishes
Let's take a look at the results!

Code: Select all

rank 1 team bluekitty, milk finishes 252
rank 2 team Cadi, [D] Cadi finishes 198
rank 3 team ΌℬitO➏34, Obito634 finishes 159
rank 4 team Sir Knuddel, Lady Knuddel finishes 150
rank 5 team Xploit, valex32 finishes 148
rank 6 team [D] Lifkug, Lifkug finishes 143
rank 7 team Super, Frosch finishes 132
rank 8 team [D]FiL, FiL finishes 126
rank 9 team Shocker, Pwner finishes 116
rank 10 team gB. | Zerodin, Hindu finishes 113
rank 11 team FerzaK, FUMOFFU finishes 110
rank 12 team mB. | maple, mB. | dinner finishes 107
rank 13 team Armin v Buuren, his cat finishes 103
rank 14 team «Èni®õ§|*>, Nõ GõõD «È finishes 103
rank 15 team gB. | FeIs, gB. | Kayumi finishes 103
rank 16 team [D] Themix, Themix finishes 103
rank 17 team Puncha, Yung Puncha finishes 102
rank 18 team BsRobert, BsRobert Dummy finishes 100
rank 19 team Teengoten, Snitchy finishes 97
rank 20 team Antro, .:Danytto:. finishes 88
rank 21 team ENIAC, Zadaxs finishes 84
rank 22 team <BµmM>, dummy finishes 81
rank 23 team phacrum, 645654 finishes 81
rank 24 team Ninja_Valik, Gr1t1sh finishes 80
rank 25 team Bixes, Themix finishes 79
rank 26 team maggi323, Forris finishes 79
rank 27 team Obst, Ark :3 finishes 77
rank 28 team MMilos, Wombat finishes 77
rank 29 team jet`s dummy, .:JeT:. finishes 76
rank 30 team Sweeeeet GM :>, Sweeeeet D. :> finishes 75
rank 31 team shoxX, Themix finishes 74
rank 32 team 'qZ |BlaGK|›, Chairn finishes 73
rank 33 team Chairn, |BlaGK|› finishes 72
rank 34 team ٭ıƞdex'<3, |Ŗaþŧurę| finishes 70
rank 35 team koala_viech, Lifkug finishes 68
rank 36 team QzBoY, Bes finishes 66
rank 37 team Illyoviszcztee, gr33nt3a finishes 65
rank 38 team qwerty, Armin v Buuren finishes 65
rank 39 team Mannyking, Sky-Owl finishes 64
rank 40 team Cr3amy, Cr3amL0v3r finishes 64
rank 41 team Juandissimo, Dr4gg3r finishes 64
rank 42 team FerzaK, Shotokan finishes 64
rank 43 team Novo c:, hi_leute_gll finishes 63
rank 44 team brain, LanuX finishes 63
rank 45 team still nameless, (1)still namele finishes 62
rank 46 team CookieAlex, CookieMichal finishes 60
rank 47 team deen, Lady Saavik finishes 59
rank 48 team [D] RedFight, RedFight finishes 59
rank 49 team Pio, Themix finishes 58
rank 50 team Ñı©Ø, Ñı©Ø ÐUMMY finishes 58
002 - What is the most common mapper team on DDNet?
The second analysis and I already failed the attempt to gradually increase the difficulty of the analyses. This is similar to the last analysis but we want to find out the most common team of mappers this time. I won't provide the code here since its very similar to the code of the last analysis and even easier. You can check out the code on github if you really want to see it. But here are the results.

Code: Select all

rank 1 team Broken, Intercity, Bee maps 7
rank 2 team Kintaro*, Delo maps 4
rank 3 team Fuu, Zoo maps 4
rank 4 team Bixes, Themix maps 4
rank 5 team Kintaro*, =CuBe=, =Typhoon= maps 3
rank 6 team Skeptar, tobu. maps 3
rank 7 team sMo0g, TheZyZya maps 3
rank 8 team Alias, Bee maps 3
rank 9 team Broken, Waschlappen maps 2
rank 10 team Cøke, Moby Dog maps 2
rank 11 team KaB, Tsin maps 2
rank 12 team Juandissimo, IRGNW maps 2
rank 13 team Chill [TD], DoNe maps 2
rank 14 team Ezio, Rico maps 2
rank 15 team DoNe, Various Mappers maps 2
rank 16 team SonGoku, spimm maps 2
rank 17 team Kayumi, {E}{L}{I}{T}{E} maps 2
rank 18 team Wolƒ.▲, Elephant maps 2
rank 19 team Hitomi, Saavik maps 2
rank 20 team Ama, Hitomi maps 2
003 - Which mapper has the most finishes on his own maps?
Every mapper gets a list of all his maps, we also create a counter which holds the finishes of his own maps which we will use later.

Code: Select all

mappers = {}
for m in maps:
    for mapper in m["mappers"]:
        if mapper not in mappers:
            mappers[mapper] = {"fin_own":0, "maps":[m["name"]]}
        else:
            mappers[mapper]["maps"].append(m["name"])
            
# poor saavik has another mapper than ingame name :(
mappers["Lady Saavik"] = mappers["Saavik"]
del mappers["Saavik"]
Now we go over the finishes and increase the counter of own map finishes if we find a finish were a player finished his own map.

Code: Select all

for f in finishes:
    p = f["player"]
    if p in mappers:
        if f["map"] in mappers[p]["maps"]:
            mappers[p]["fin_own"] += 1
And now the sorting and printing.

Code: Select all

mappers = sorted(mappers.items(), key=lambda x: x[1]["fin_own"], reverse=True)
for rank, (mapper, info) in enumerate(mappers[:50]):
    print "rank", rank+1, "mapper", mapper, "has", info["fin_own"], "finishes on his own maps"
Let's get the results!

Code: Select all

rank 1 mapper Broken has 151 finishes on his own maps
rank 2 mapper DoNe has 110 finishes on his own maps
rank 3 mapper Ama has 109 finishes on his own maps
rank 4 mapper Themix has 102 finishes on his own maps
rank 5 mapper Welf has 83 finishes on his own maps
rank 6 mapper Knight :3 has 53 finishes on his own maps
rank 7 mapper Aoe has 47 finishes on his own maps
rank 8 mapper mikey12 has 39 finishes on his own maps
rank 9 mapper Hitomi has 38 finishes on his own maps
rank 10 mapper SBL has 34 finishes on his own maps
rank 11 mapper Vasten100 has 33 finishes on his own maps
rank 12 mapper Destoros has 33 finishes on his own maps
rank 13 mapper Juandissimo has 33 finishes on his own maps
rank 14 mapper Seba0006 has 31 finishes on his own maps
rank 15 mapper fikmesån has 31 finishes on his own maps
rank 16 mapper Silex has 29 finishes on his own maps
rank 17 mapper Ñı©Ø has 29 finishes on his own maps
rank 18 mapper Lady Saavik has 29 finishes on his own maps
rank 19 mapper Hindu has 28 finishes on his own maps
rank 20 mapper Micro has 28 finishes on his own maps
rank 21 mapper Chill [TD] has 27 finishes on his own maps
rank 22 mapper hannibal has 27 finishes on his own maps
rank 23 mapper <BµmM> has 26 finishes on his own maps
rank 24 mapper [A] Awesome has 26 finishes on his own maps
rank 25 mapper =CuBe= has 25 finishes on his own maps
rank 26 mapper cris has 25 finishes on his own maps
rank 27 mapper KBeeeR has 24 finishes on his own maps
rank 28 mapper Suppe has 24 finishes on his own maps
rank 29 mapper ٭ıƞdex'<3 has 21 finishes on his own maps
rank 30 mapper Meliodafu has 21 finishes on his own maps
rank 31 mapper !_Vergeboy_! has 21 finishes on his own maps
rank 32 mapper SickCunt has 19 finishes on his own maps
rank 33 mapper Borup has 19 finishes on his own maps
rank 34 mapper Bixes has 19 finishes on his own maps
rank 35 mapper Chicken has 18 finishes on his own maps
rank 36 mapper Fňokurka oo7 has 18 finishes on his own maps
rank 37 mapper Tuna has 17 finishes on his own maps
rank 38 mapper Skeptar has 17 finishes on his own maps
rank 39 mapper Gridwyn has 16 finishes on his own maps
rank 40 mapper Åmol has 16 finishes on his own maps
rank 41 mapper Cyberpunk has 15 finishes on his own maps
rank 42 mapper »Rogue. has 15 finishes on his own maps
rank 43 mapper Alvin Risk has 15 finishes on his own maps
rank 44 mapper RaiNy has 13 finishes on his own maps
rank 45 mapper Cøke has 13 finishes on his own maps
rank 46 mapper 645654 has 13 finishes on his own maps
rank 47 mapper Kosho has 12 finishes on his own maps
rank 48 mapper aMu has 12 finishes on his own maps
rank 49 mapper EpicIceTee has 11 finishes on his own maps
rank 50 mapper hi_leute_gll has 10 finishes on his own maps
I'm on holiday until around 12.8. until then there will be no more analyses.

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EryX
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Re: DoNe does data analysis

Post by EryX »

Nice, is it also possible to show, how many minutes a player spent on all servers?
I could sum up all times, but ofc there are often more than only 1 try.
Are the other tries saved, too?
Would be nice to know how many minutes i spent on DDNet with "EryX" :)

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timakro
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Re: DoNe does data analysis

Post by timakro »

EryX wrote:Nice, is it also possible to show, how many minutes a player spent on all servers?
I could sum up all times, but ofc there are often more than only 1 try.
Are the other tries saved, too?
Would be nice to know how many minutes i spent on DDNet with "EryX" :)
No, unfortunately they aren't saved.

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deen
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Re: DoNe does data analysis

Post by deen »

Some ideas:
  • Most points made on one day/week/month by which player and when?
  • Map that was unfinished for the longest time since release
  • Person who plays in a team with the most different people
  • Time of day when players finish maps (finding night owls etc)

Chairn
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Re: DoNe does data analysis

Post by Chairn »

Meh, blagk and i should be 6th :(

Nice analysis btw ^^.

hannibal
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Re: DoNe does data analysis

Post by hannibal »

Who finished the most in team 0! Who played the least with his dummy!

Nirvana
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Re: DoNe does data analysis

Post by Nirvana »


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deen
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Re: DoNe does data analysis

Post by deen »

I have asked timakro if the git repo can be uploaded again.

Nirvana
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Re: DoNe does data analysis

Post by Nirvana »

deen wrote:
Tue Jul 07, 2020 11:40 am
I have asked timakro if the git repo can be uploaded again.
Thank you.

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timakro
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Re: DoNe does data analysis

Post by timakro »

Can't find it on my disk so it's probably lost forever :/

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