Welcome back everyone to the scouting series. In the previous piece we focused on how players perform in full buy rounds, and now we will finish the economic dimensions by including performance in anti-ecos and force buys. Just like in the previous pieces, we will be using data from 69 different tournaments, and 4590 maps, including 476 players with more than 30 maps, all over 2024. First, we will use six of the estimated metrics to separate players in three key aspects of anti-eco rounds: players that are being punished in ecos (KPR vs DPR), deaths without risk (DPR vs Deaths Traded), and multi-kills with real impact on the round (Multi-Kill Score vs Multi-Kill Win Rate). Secondly, we will be doing comparisons with 2 players between their performance in full buys, and their performance in anti-ecos, showing why it is important to disaggregate metrics depending on the economy of both teams. Finally, we will look for the best replacements for Niko in G2 using the metrics for the economy dimensions and building a similarity score.
The metrics extracted from demos that we will be using throughout this piece are the same ones that we used in the second element of the series (Full Buys), which were: Kills per Round (KPR), Deaths per Round (DPR), Trade Kills per Round (TKPR), Percentage of Deaths Traded (DTR), Average Damage per Round (ADR), Opening Attempts per Round (OPAT), Opening Kills per Round (OPKPR), Opening Deaths per Round (OPDPR), Opening Deaths Traded (EDTR), Win Rate in rounds with opening kill (EK WR), Percentage of bullets that are headshot (HSBUL), Multi-Kill Score per round (MKPR) and Win Rate in rounds with multi-kills (MK WR). The final section will use these metrics for all of three economy situations, as well as CT and T sides, which adds up to 78 different metrics.
Differentiating players using scatterplots
KPR vs DPR: looking for eco-griefers
To identify players most vulnerable or impactful during anti-eco and half-buy rounds, we plot Kills Per Round (KPR) against Deaths Per Round (DPR) (with inverted DPR values—higher means fewer deaths). Here’s a breakdown of the key clusters:
- Low KPR, Low DPR (Bottom-Left):
Includes IGLs like HooXi, Snax, apEX, Snappi, karrigan, cadiaN, siuhy. They focus on strategy over individual impact and are often tested in low-economy rounds. - High KPR, High DPR (Top-Right):
Star players like m0NESY, ZywOo, Twistzz, ropz excel here, securing kills while minimizing deaths. blameF stands out for high KPR but slightly weaker DPR. - Low KPR, High DPR (Top-Left):
Passive players such as Aleksib, Jame, Brollan, hallzerk, 910, FalleN, wonderful prioritize survivability over fragging. - High KPR, Low DPR (Bottom-Right):
Aggressive players like BOROS, jkaem, maden, electronic, dupreeh, mopoz, EliGe, donk, NertZ secure kills but are punished for risky decisions.
This visualization highlights the trade-offs players face in anti-eco situations—between aggression, impact, and survivability.
DTR vs TKPR: union makes force
Next, we assess whether players are being traded effectively, minimizing the impact of their deaths on the round outcome. The analysis groups players into four key categories:
- High Deaths, High Trades (Top-Left):
Players frequently die but are often traded, indicating roles as aggressive initiators seeking information or man advantages. Examples include HooXi, BOROS, karrigan, dupreeh, rain, Snax, Elige. - Low Deaths, Low Trades (Bottom-Right):
Players here die rarely, but their deaths lack follow-up trades. This group includes AWPs like SunPayus, m0NESY, Jame, FalleN, and riflers like ropz, arrozdoce, Twistzz. - Low Deaths, High Trades (Top-Right):
These players combine survivability with high trade potential, often featuring clutch AWPs like wonderful, MartinezSa, ZywOo, woxic, 910. - High Deaths, Low Trades (Bottom-Left):
Players in this cluster die frequently without being traded, highlighting a need for adjustment in their approach. Examples include maden, Snappi, and others like dexter, mopoz, xertioN, electronic, jkaem, YEKINDAR, xKacpersky.
This breakdown reveals the roles and impact of players in anti-eco situations, offering insights into team strategies and individual performance.
MK-SCORE vs MK-WR: securing rounds
Finally, we analyze which players secure rounds for their teams during anti-ecos. Players in the top-right corner excel with high multi-kill scores and strong win rates in such rounds. Notable examples include:
iM, jkaem, adamS, malbsMD, blameF, Ax1Le, stavn, mopoz, dexter, electronic, EliGe, donk.
This analysis reveals new insights: players like mopoz, initially thought to be overcommitting, are crucial in converting anti-ecos into wins. However, players like Snappi and maden stand out for their low win rates in multi-kill rounds, signaling room for improvement.
Addressing the differences between eco cobras and overall high fraggers
The idea in this section is look for players whose playstile changes significantly depending on the buytype of the opponent. If the spider graph shows considerable differences, we can be pretty sure that they are guilty of charge.
He is not beating the allegations: blameF
Firstly, we have the most frequent suspect for eco-fragging: blameF. The player from FNATIC has been accused constantly of boosting stats on ecos, and we can see that the eye test matches the numbers test. blameF scores significantly higher on KPR, MK SCORE, MK WR, ADR, OPAT and OPKPR on anti-ecos than on full buys. He is a highly passive player on full buys, with low multi-kill score and low win rate for multi kills, but turns into an extremely aggressive player on anti-ecos. This is not a problem for FNATIC, since he is making sure they don’t lose rounds, but raise the question of whether he could be doing more to help his team win on full buy situations.
Being impactful when it matters: NiKo
Now, let’s contrast blameF with an opposite type of player: NiKo from G2 Esports. On full buys, NiKo is a reasonably aggressive player, looking for opening kills and therefore scoring high in OPAT and OPKPR. On the contrary, he is much more passive on anti-ecos, where he presents less opening attempts. In terms of killing, he is extremely good at ADR, MK SCORE, KPR and DPR in full buys, and performs slightly worse on anti-ecos. This does not mean that NiKo is worse at anti-ecos, we all known he could farm those easily, he is probably being cautious with the risks that he is taking, and the other team probably don’t want to run into NiKo with lesser weapons.
Replacing NiKo: Which are G2’s best options?
Using granular metrics to find similar players
In the last part of this series, we used a custom formula to identify specific player profiles. Now, let’s imagine stepping into TaZ and G2’s staff’s shoes. With NiKo’s recent departure to Team Falcons being confirmed earlier this month, G2 faces the challenge of replacing one of CS:GO’s most iconic players while preserving their winning formula.
To tackle this, we analyze 78 metrics, covering CT and T sides as well as force-buy rounds, all converted into quantiles. Using Euclidean distance, we measure how similar players are to NiKo. Scores range from 0 (completely different playstyles) to 100 (identical), helping pinpoint the best replacements. For example, Skullz and donk represent polar opposites, with Skullz as a passive lurker/anchor and donk as an aggressive playmaker.
Top 10 NiKo Alternatives
The following players have the highest similarity scores with NiKo, though not all are perfect fits:
- Best Option: xertioN
A young, aggressive, and mechanically skilled rifler, xertioN has playoff experience with MOUZ this year. However, his rising demand will likely make him a costly acquisition. - Solid Alternative: XANTARES
A proven rifler with exceptional consistency, XANTARES excels at opening bombsites. But his loyalty to Eternal Fire and their mission to represent Turkey globally may make a transfer unlikely. - Backup Plans: roeJ and REZ
Reliable players with solid performances, though they compete at a lower level and lack the star power of NiKo. These options could work as stopgaps if a better fit isn’t secured, but they would represent a clear downgrade.
By leveraging data and evaluating similarity scores, G2 can strategically approach its search for a NiKo replacement, ensuring its team remains competitive moving forward.
Three Ukrainians with vastly different profiles also make the list:
- b1t:
An incredible fit for G2, but his departure from NaVi seems highly unlikely given their success, legendary status, and strong Ukrainian roots. - npl:
Signing npl would signal a shift in G2’s mindset—bringing in a young IGL thriving in tier 2 scenes. Pairing him with a talented anchor like perfecto could further solidify the roster. - Jambo:
A standout rookie of the Shanghai Major cycle. It’s only a matter of time before he’s picked up by a major team, making him a promising option for G2.
Other Notable Names
- FL4MUS:
A key contributor to GamerLegion’s miracle Major run. Coach ash highlighted his aggressiveness in full-buy rounds and reliability in anti-ecos, matching the spider graph results. - bnox and ZEDKO:
Despite being similar to NiKo statistically, their limited growth in tier 2/3 scenes suggests their ceiling isn’t high enough for a top-tier team like G2.
malbsMd as a Potential Replacement?
G2’s CEO hinted that malbsMd was signed with a potential transition in mind. His similarity score with NiKo is 58%, but as NiKo’s teammate, his role has been constrained by NiKo’s presence. While malbsMd could fill NiKo’s shoes with his solid mechanics, this would create a gap for a new hard-entry fragger, complicating the equation.
While data-driven scouting provides valuable insights, context remains crucial. Metrics must align with the team’s strategic vision and situational or specialist dimensions—discussed in future pieces—are essential for completing the analysis. Stay tuned for the next part of this series!
