hi. i have a dataset with real time cs:go game data in given format:
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id,round_number,game_time,round_time,round_period,winner_side,ct_score,t_score,ct_players_alive,t_players_alive,ct_equipment_value_sum,t_equipment_value_sum,ct_health_sum,t_health_sum,ct_armors,t_armors,ct_helmets,t_helmets,ct_defuse_kits,t_c4s,ct_flashbangs,t_flashbangs,ct_smokes,t_smokes,ct_hes,ct_incendiaries,t_molotovs,ct_glock_18s,t_glock_18s,ct_usp_ss,t_usp_ss,ct_p2000s,t_p2000s,ct_p250s,t_p250s,ct_dual_berettas,t_dual_berettas,ct_p90s,t_p90s,ct_mp5_sds,t_mp5_sds,ct_mp7s,t_mp7s,ct_pp_bizons,t_pp_bizons,ct_mp9s,t_mp9s,ct_ump_45s,t_ump_45s,ct_mac_10s,t_mac_10s,ct_augs,t_augs,ct_m4a4s,t_m4a4s,ct_sg_553s,t_sg_553s,ct_m4a1s,t_m4a1s,ct_ak_47s,t_ak_47s,ct_famases,t_famases,ct_galil_ars,t_galil_ars,ct_g3sg1s,t_g3sg1s,ct_scar_20s,t_scar_20s,ct_awps,t_awps,ct_ssg_08s,t_ssg_08s,ct_m249s,t_m249s,ct_negevs,t_negevs,ct_xm1014s,t_xm1014s,ct_mag_7s,t_mag_7s,ct_sawed_offs,t_sawed_offs,ct_novas,t_novas,ct_zeus_x27s,t_zeus_x27s,game_id,created_at,updated_at
17124,1,102687498240,-16296878080,0,T,0,0,5,5,2450,2450,500,500,1,1,0,0,1,1,1,1,0,1,1,0,0,0,5,4,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,2023-03-03 17:20:18.546591,2023-03-03 17:20:18.546591
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i',m trying to create a system that can predict the each round outcome of cs:go game, the label is the field with name `winner_side_CT`.

In my dataset, I have time series data for each round of CS:GO. The data is easier to predict closer to the end of a round (round time is stored in round_time), but more difficult at the beginning. I expect that the closer to the beginning of the round, the closer the probability of winning predicted by the neural network is to 50%. However, after the accuracy of the neural network became ~88%, all the results of predictions are much closer to 100%. also my dataset has much more samples for the beginning of the round.
 How can I solve this? 