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Requirements
pandas,numpy,matplotlib,seaborn

Using data from https://pubgtracker.com/ to analyse best strategies to play PUBG

Download datasets from https://drive.google.com/file/d/1E7yHRPW04sAc3mu90jbPHdiy9MWKCcO3/view

From the first heat map we can conclude that:

Most fighting features seem to matter
killPoints (Kill Rating for Ingame Statistic/Leaderboard) are not that important
teamKills are friendly fire kills
killPlace is the most Important feature so far

From the second Heatmap we can see that

walkDistance seems very important, which makes sense: you can only walk around if you are still alive!
Same for swim and rideDistance but not that important
winPoints you gain are not that much connected to your actual surviving time


So the conclusion from this analysis is

To survive longer in PUBG, you should be able to fight well, but also acquire many weapons.
In general walking should be the preferred way to move.
Even noobs know this :) 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir(os.getcwd()+'\\pubgstats'))
['sample_submission_V2.csv', 'test_V2.csv', 'train_V2.csv']

train = pd.read_csv(os.getcwd()+'\\pubgstats'+'\\train_V2.csv')
train
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
Id groupId matchId assists boosts damageDealt DBNOs headshotKills heals killPlace ... revives rideDistance roadKills swimDistance teamKills vehicleDestroys walkDistance weaponsAcquired winPoints winPlacePerc
0 7f96b2f878858a 4d4b580de459be a10357fd1a4a91 0 0 0.000 0 0 0 60 ... 0 0.0000 0 0.000 0 0 244.80 1 1466 0.4444
1 eef90569b9d03c 684d5656442f9e aeb375fc57110c 0 0 91.470 0 0 0 57 ... 0 0.0045 0 11.040 0 0 1434.00 5 0 0.6400
2 1eaf90ac73de72 6a4a42c3245a74 110163d8bb94ae 1 0 68.000 0 0 0 47 ... 0 0.0000 0 0.000 0 0 161.80 2 0 0.7755
3 4616d365dd2853 a930a9c79cd721 f1f1f4ef412d7e 0 0 32.900 0 0 0 75 ... 0 0.0000 0 0.000 0 0 202.70 3 0 0.1667
4 315c96c26c9aac de04010b3458dd 6dc8ff871e21e6 0 0 100.000 0 0 0 45 ... 0 0.0000 0 0.000 0 0 49.75 2 0 0.1875
5 ff79c12f326506 289a6836a88d27 bac52627a12114 0 0 100.000 1 1 0 44 ... 0 0.0000 0 0.000 0 0 34.70 1 0 0.0370
6 95959be0e21ca3 2c485a1ad3d0f1 a8274e903927a2 0 0 0.000 0 0 0 96 ... 0 0.0000 0 0.000 0 0 13.50 1 1497 0.0000
7 311b84c6ff4390 eaba5fcb7fc1ae 292611730ca862 0 0 8.538 0 0 0 48 ... 0 2004.0000 0 0.000 0 0 1089.00 6 1500 0.7368
8 1a68204ccf9891 47cfbb04e1b1a2 df014fbee741c6 0 0 51.600 0 0 0 64 ... 0 0.0000 0 0.000 0 0 799.90 4 0 0.3704
9 e5bb5a43587253 759bb6f7514fd2 3d3031c795305b 0 0 37.270 0 0 0 74 ... 0 0.0000 0 0.000 0 0 65.67 1 0 0.2143
10 2b574d43972813 c549efede67ad3 2dd6ddb8320fc1 0 0 28.380 0 0 0 75 ... 0 0.0000 0 0.000 0 0 868.30 9 0 0.3929
11 8de328a74658a9 f643df9df3877c 80170383d90003 0 0 137.900 1 0 0 64 ... 0 0.0000 0 0.000 0 0 451.70 1 0 0.4043
12 ce4f6ac165705e da24cdb91969cc 535b5dbd965a94 0 0 0.000 0 0 0 37 ... 0 6639.0000 0 0.000 0 0 2784.00 6 0 0.9286
13 b7807186e3f679 3c08e461874749 2c30ddf481c52d 0 1 324.200 0 1 5 5 ... 0 1228.0000 0 76.840 0 0 2050.00 6 1462 0.8750
14 8e244ac61b6aab d40d0c7d3573a1 94e1c1cc443c65 0 1 122.800 1 0 2 25 ... 1 1237.0000 0 60.290 0 0 1666.00 5 1531 0.9000
15 12d8d4bd94312c fe52d481bae68b 6fd9e765ddd0c5 0 0 80.710 1 0 0 72 ... 1 0.0000 0 0.000 0 0 105.10 5 0 0.2766
16 62f2f0917d84b2 f61b698274d9f5 1d6cfe0f6f23b0 0 2 81.710 1 0 14 25 ... 0 519.9000 0 0.000 0 0 3674.00 7 0 0.7308
17 92022479b92ce7 2f2c33f548c4b9 07948d723b9c0f 0 3 254.300 0 0 12 13 ... 0 2367.0000 0 15.290 0 0 1787.00 3 0 0.8211
18 7bd224781f064b 6dde607d151819 733af30cc00099 0 0 0.000 0 0 0 79 ... 0 0.0000 0 0.000 0 0 137.40 2 0 0.1923
19 71cbdbc3b263e5 7b61f74b51906c a329ac99449ad7 0 1 65.280 0 0 1 48 ... 0 0.0000 0 20.850 0 0 3310.00 3 1479 0.9310
20 02ace8c6e58461 a4bc548028f800 80f2b8448e474b 0 4 269.100 0 1 8 18 ... 1 2734.0000 0 0.000 0 0 1794.00 5 0 0.6383
21 00341b1caa5420 d661a2d19e7ae9 f3956286eb39a5 0 0 158.700 1 0 0 75 ... 0 0.0000 0 0.000 0 0 580.10 2 0 0.2143
22 9b2961d4d51f91 799d0a4d61dc3c e833ca2282169d 0 1 192.300 1 2 3 15 ... 0 2332.0000 0 0.000 0 0 1264.00 4 1494 0.7500
23 0b6fbdfb59c994 7a75c3e86934f6 8b0a78c005cea0 0 6 1011.000 6 2 2 2 ... 0 4860.0000 0 0.000 0 0 2727.00 7 1603 0.9592
24 736eda9b9c20b3 d35e80e4e64dd4 62fbe726028662 0 3 327.600 4 1 1 3 ... 0 0.0000 0 0.000 0 0 3503.00 4 1496 0.9231
25 4c45dc732689ec 8e0a0ea95d3596 37f43ba55ec0a4 1 4 558.600 3 0 4 11 ... 0 1183.0000 0 0.000 0 0 2711.00 7 1494 0.8696
26 91f5da9c3628eb 090fd12f3ca8a8 e8b8cf5d9231d3 0 0 44.280 0 0 0 78 ... 0 0.0000 0 0.000 0 0 15.30 0 0 0.1154
27 dbf611495bfda3 10cbb86844dee0 2cdae31ee18601 0 4 381.200 2 1 2 7 ... 0 1798.0000 0 0.000 0 0 1933.00 4 0 0.7234
28 f9473c4f1cfdc4 8483976f3ba230 6057f846f3ed12 0 6 345.600 2 1 1 6 ... 0 0.0000 0 0.000 0 0 3855.00 4 0 0.9630
29 ac5b57ff39979c 857cc55b2b6001 e019e04dee4f19 0 0 0.000 0 0 0 87 ... 0 0.0000 0 0.000 0 0 0.00 0 0 0.0000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4446936 ee62630c3a17e3 cc6b1b4264eb73 269a041abb69a5 0 1 68.200 0 1 1 22 ... 0 0.0000 0 34.860 0 0 2708.00 7 0 0.7308
4446937 68100cdb23f1f0 9b8970931c5d00 ce5a23d8bb7883 1 2 127.400 1 1 4 31 ... 0 0.0000 0 0.000 0 0 1364.00 5 0 0.7111
4446938 7718e7c0c355a3 54d5ce5a79e0f6 06def1c4d808d4 0 0 0.000 0 0 0 64 ... 0 0.0000 0 72.210 0 0 173.10 1 0 0.0385
4446939 36b218fd209b00 195337a8c2ae1d fa1b1885f56b7d 0 0 151.500 1 0 0 35 ... 0 0.0000 0 0.000 0 0 56.14 1 1539 0.3830
4446940 3eefd3ee81154a 19b7a730468b55 31045b7b933f3d 0 1 0.000 0 0 1 58 ... 0 2728.0000 0 0.000 0 0 1362.00 6 0 0.6250
4446941 18e04b3b452a1a 8de4310ab2d2ae 054bfeb4d51fc4 0 0 62.350 0 0 0 79 ... 0 0.0000 0 0.000 0 0 65.21 2 0 0.1600
4446942 2c9f1610de0ecd d64a0663e96058 5c9254fa96f53e 0 4 724.700 6 4 14 1 ... 3 5076.0000 0 0.000 0 0 2162.00 8 0 1.0000
4446943 0f0dd3fe907cef 5f251817449ae7 cf837481bd01f3 0 0 0.000 0 0 0 82 ... 0 0.0000 0 0.000 0 0 57.59 2 0 0.1111
4446944 914aec03b107db a8c5116da13d88 02dd2c1a0b34de 0 0 175.000 0 1 0 29 ... 0 2532.0000 0 0.000 0 0 1349.00 5 0 0.6875
4446945 e8b6ed3ec93a76 3e5b779bd7cf12 95e5611e58f4d5 0 0 0.000 0 0 0 81 ... 0 0.0000 0 0.000 0 0 57.19 1 0 0.1875
4446946 f1aca3f5aeafd8 2c6765c0fc6d77 84d7e32c95913a 0 0 0.000 0 0 0 53 ... 0 0.0000 0 0.000 0 0 2591.00 7 0 0.7292
4446947 cac9fe367120a1 d1398e8c0941f3 a27caa11cb4dfb 0 0 0.000 0 0 0 61 ... 0 0.0000 0 0.000 0 0 631.10 4 0 0.3830
4446948 445aaa1ddc858e b1efcbdb7ce674 05f6cd4077cd68 1 3 736.500 4 1 2 7 ... 0 0.0000 0 0.000 0 0 1685.00 3 1500 0.7917
4446949 138e004749faf9 dbe0096979e393 5256cd7403054e 0 0 100.000 1 0 0 32 ... 0 0.0000 0 0.000 0 0 424.60 3 0 0.1458
4446950 d05b0c4b2ff311 8248fa2552457b 88c002b589d411 0 0 203.500 0 0 0 32 ... 0 0.0000 0 0.000 0 0 1559.00 5 0 0.5000
4446951 0381eae18c429f c0df2e78ccce86 be06c0c5f9a47e 0 0 0.000 0 0 0 85 ... 0 0.0000 0 0.000 0 0 44.90 1 0 0.1000
4446952 78b990601cafb6 aa64828a68bc21 8496e878b7ee1d 0 0 0.000 0 0 0 44 ... 0 0.0000 0 5.328 0 0 1177.00 5 0 0.8462
4446953 372304ea470cad 0db6cf38e79c9e a530fd807f535a 0 0 30.100 0 0 0 57 ... 1 0.0000 0 0.000 0 0 1025.00 5 1551 0.5926
4446954 894c01c8e4524f c33e793af077f9 deb3a91c03d0f3 0 0 30.100 0 0 0 58 ... 0 0.0000 0 0.000 0 0 2146.00 6 1502 0.5306
4446955 b9155a229aedfd 570d9414a536f3 0c5ab888689674 0 0 0.000 0 0 0 60 ... 0 604.8000 0 0.000 0 0 1158.00 3 0 0.4792
4446956 dae05e0d743059 3902915a7a1943 97b64a07c05761 1 0 151.900 0 0 1 77 ... 1 0.0000 0 0.000 0 0 828.30 7 0 0.1071
4446957 2a4163ccbe0e3b 2689c981578849 eebc058a45ff13 0 1 100.000 0 0 0 32 ... 1 0.0000 0 0.000 0 0 363.70 2 0 0.4583
4446958 837349af7e8a35 58bc4104935623 2001300d4f5787 0 0 0.000 0 0 0 92 ... 0 0.0000 0 0.000 0 0 0.00 0 0 0.0000
4446959 d29bfa313ad766 ac3f1b4a56e5ad 2f3b1af94739b3 0 0 22.680 0 0 0 89 ... 0 0.0000 0 0.000 0 0 40.25 1 0 0.0842
4446960 69fa4c2d5431b1 2a3ad0e37fb6ce 818ccf2160343f 0 0 327.700 3 2 0 4 ... 0 180.4000 0 0.000 0 0 845.60 3 0 0.2414
4446961 afff7f652dbc10 d238e426f50de7 18492834ce5635 0 0 0.000 0 0 0 74 ... 0 1292.0000 0 0.000 0 0 1019.00 3 1507 0.1786
4446962 f4197cf374e6c0 408cdb5c46b2ac ee854b837376d9 0 1 44.150 0 0 0 69 ... 0 0.0000 0 0.000 0 0 81.70 6 0 0.2935
4446963 e1948b1295c88a e26ac84bdf7cef 6d0cd12784f1ab 0 0 59.060 0 0 0 66 ... 0 0.0000 0 2.184 0 0 788.70 4 0 0.4815
4446964 cc032cdd73b7ac c2223f35411394 c9c701d0ad758a 0 4 180.400 1 1 2 11 ... 2 0.0000 0 0.000 0 0 2748.00 8 0 0.8000
4446965 0d8e7ed728b6fd 8c74f72fedf5ff 62a16aabcc095c 0 2 268.000 0 0 1 18 ... 0 1369.0000 0 0.000 0 0 1244.00 5 0 0.5464

4446966 rows × 29 columns

train.isnull().values.any()
True

list(train)
['Id',
 'groupId',
 'matchId',
 'assists',
 'boosts',
 'damageDealt',
 'DBNOs',
 'headshotKills',
 'heals',
 'killPlace',
 'killPoints',
 'kills',
 'killStreaks',
 'longestKill',
 'matchDuration',
 'matchType',
 'maxPlace',
 'numGroups',
 'rankPoints',
 'revives',
 'rideDistance',
 'roadKills',
 'swimDistance',
 'teamKills',
 'vehicleDestroys',
 'walkDistance',
 'weaponsAcquired',
 'winPoints',
 'winPlacePerc']

# DBNO means downed but not out
import seaborn as sns

f1 = (
    train.loc[:, ["winPlacePerc","killPoints", "killPlace", "kills", "DBNOs",  "headshotKills", "assists", "longestKill","heals", "revives" , "teamKills" ]]
).corr()
plt.figure(figsize=(14, 12))
sns.heatmap(f1, annot = True)
<matplotlib.axes._subplots.AxesSubplot at 0x2cd43a489b0>

png

train["teamKills"].describe()
count    4.446966e+06
mean     2.386841e-02
std      1.673935e-01
min      0.000000e+00
25%      0.000000e+00
50%      0.000000e+00
75%      0.000000e+00
max      1.200000e+01
Name: teamKills, dtype: float64

train["kills"].describe()
count    4.446966e+06
mean     9.247833e-01
std      1.558445e+00
min      0.000000e+00
25%      0.000000e+00
50%      0.000000e+00
75%      1.000000e+00
max      7.200000e+01
Name: kills, dtype: float64

f2 = (
    train.loc[:, ["winPlacePerc","winPoints", "maxPlace", "numGroups", "walkDistance", "swimDistance" , "rideDistance", "roadKills", 'vehicleDestroys', "weaponsAcquired" ]]
).corr()
plt.figure(figsize=(12, 10))
sns.heatmap(f2, annot = True)
<matplotlib.axes._subplots.AxesSubplot at 0x2cd42532ba8>

png

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Extracting statistics from PUBG data

medium.com/cyberczcodes/use-python-and-heat-maps-to-prepare-chicken-dinner-22c8330a96e0

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numpy pandas seaborn pubg pubgtracker

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