There are two guides in this thread. The first is on common archetypes. The second is my personal approach on offensive teambuilding, which is a pdf attached to this post.
Table of Contents (linked)
Introduction
Hi Everyone,
I recently wrote a program that does processing of sorts on teambuilders. Since then, I've been improving the program to slowly incorporate more varied statistics, to the point where I felt it could do a pretty comprehensive analysis on the metagame.
Goals
In this article, I am primarily interested in understanding team building in this metagame from a data-driven approach supported by experience, and sharing this knowledge. Fundamentally, I want to bridge the gap between a team dump that most players will be unwilling to share to keep their competitive advantage (and rightfully so), and the less experienced player who wants model teams to learn the metagame from. While team building guides such as this are useful for fitting pokemon into roles, they address little about their interactions.
Yet, understanding interactions is fundamental to good and creative teambuilding, not only from a position of advancing one's gameplan through the use of synergies but also defending against an opponent's gameplan. It is impossible to cover every mon that can be thrown out by one's opponent perfectly, so instead of covering every individual mon equally, many good creative teams focus on the big picture and find outs against opposing archetypes.
To this end, I will try to answer the questions: what are the broad archetypes that exist in the metagame? which pokemon and what sets go well together, and why? Which pokemon do not appear together, and why? In answering the latter question, I also hope to make a comparative analysis, meaning why choose one mon on a team over another?
To study these interactions, I wrote a program that takes a teambuilder, categorizes sets, measures the synergy across sets, and ranks them in order of importance. I have also made an attempt at categorizing these sets into Archetypes. In doing this analysis, I hope that the processed data can be a focal point for discussion, and in so become a teambuilding guide of its own. I have thus invited UD to contribute, scrutinize the results, and talk about any trends and interesting cores.
What This is Not
This is not an article for one to scout my sources. I take the confidentiality of my sources seriously, and I have combined and anonymized the results into groups. I have only released individual data if my source has consented to it. The focus will be on what and how cores synergize, and not on what these players prefer to use.
Data Sources
I have obtained generous team contributions from thelinearcurve, Astamatitos, McMeghan, Golden Sun, and UD, all of whom are notable players and builders in their own right. However, since these teams are not publicly accessible, a good reference for this discussion is thelinearcurve's public builder.
What is a Core, and what is an Archetype?
A core is a group of mons that function well together. In other words, they have synergy. An archetype is a group of teams that encompass a similar gameplan. Archetypes are frequently thought of as defined by a few key mons that are supplemented by a variety of cores that can be substituted with each other. Likewise, a core may belong to more than one archetype.
Methodology
The technical details of the work are in the spoilers below. Check them out if you want to know how the sets are categorized, how the synergies are measured, and how the archetypes are determined.
Categorization and Naming of Sets
Measuring Synergy
Identifying Archetypes
Core Rankings - Synergy and Frequency
Cores can either be strongly or weakly synergistic (given by the synergy score), and they can be frequent or infrequent (given by the frequency label). The final rankings that you see in the files labelled “_synergy_sets_statistics” are a combination of both – they are determined by a weighted product with an exponent. Weighting synergy too much may cause very specific cores of BL mons to appear at the top e.g. Sun teams, while, weighting frequency gives us not much more information than single mon frequencies.
Archetypes - Confidence and Frequency
The files labelled “_archetype_statistics” are the results of an attempt to categorize synergies into broad archetypes. These archetypes are completely blind to human input, meaning I did not ask for specific criteria to be met such as requiring Tyranitar in TSS. Each archetype is labelled with a number and is tagged to a ranked list, and the rankings are again determined by two factors. This time, one of the indicators is the level of confidence that the set is in the archetype. This is a number from 0 to 1, and can be thought of in percentages where the higher the number, the more likely this mon is in the archetype. The other indicator is frequency in the entire builder (not in the archetype!). Just as I did with the cores, the final rankings are a combination of both, a weighted product. Those at the top are most likely to be in the core.
Results
I roughly sorted the teams I received into two groups, based on my understanding of the contributors' team timelines, building style, and the current state of the metagame. This allows each document to be less cluttered, so that the cores become immediately obvious. The analysis though, will be focused on the group that I feel is more current.
Four main files are produced for each group, and all data is gathered into a zip file attached called "ADV_OU_Archetypes_Cores_Data_vapicuno.zip". The first three are available in .txt, .csv and sometimes .xlsx. csv files can be open in Microsoft Excel.
1. Legend indicating set categories – “statistics_legend”
2. Set core synergy statistics (includes lead statistics) -- “synergy_sets_statistics”
3. Archetypes -- “archetype_statistics”
4. Pokemon core synergy statistics, a cruder version of (1) – “synergy_statistics”
For the group that we studied, I have also included a file of set pairs that do not appear together -- “nonappearances”. thelinearcurve has also given me the liberty of compiling an aggregated set list from his teams, so that is also available in four formats -- the full set list, and filtered lists removing 1/8, 1/16 and 1/32 of the least relevant sets. You can see for example the 1/16 set list online by clicking here, where sets are aggregated and compiled into slashed moves through the program, for example like this:
Lead Analysis
This is primarily a UD contribution, thank him!
Together, these 11 leads make up >54% of the total lead Mon appearances, or in other words, would be considered the most common leads in the game. This more or less jives with conventional ADV wisdom. These top leads have much in common – Most (except Zapdos) have early game prowess either with Spikes or heavy chip damage, and in particular Salamence/Tyranitar have great versatility, forcing opponents to spend a turn on the back seat just scouting its set.
Pursuit Tar makes a great lead because it forces out opposing lead Zapdos, can hit Skarmory extremely hard with Fire Blast, sets up Sand from the get go. Occasionally, you will find yourself matched up with an opposing lead Gengar. Typically that indicates a Hypnosis + Explosion set. Either way, they must risk hitting a 60% move and / or losing the prediction game of Pursuit vs. Crunch. In other words, it's an extremely advantageous lead scenario. Pursuit Tar also typically has one coverage move to work with (Pursuit, Crunch, and a Fire move are the presumptive first three slots), so you can threaten opposing Pert lead (rare in itself) with HP Grass, or cheeky Zapdos staying in with Rock Slide. Ice Beam is an option as well, though that move presents zero upside in a T1 situation as you must switch from opposing lead Flygon or Mence, fearing their OHKO move.
Skarmory comes in at number two to no surprise. It lays a Spike against every lead other than Zapdos. However, it risks taking upwards of 70% from opposing Tar and Mence Fire Blast. But some teams would rather get that Spike on T1 anyway, making the risk worth the reward. We will ignore the next appearance of Skarmory (Roar vs. Whirlwind usually means Drill Peck, but it could also just be a bluff or picking the wrong phazing move by accident)
Tied for third are regular four attacks Leftovers Tar (BKC Tar) and MixMence. Both make for fantastic leads. MixMence can score a huge T1 advantage if faced off against opposing Tar, who is more or less obligated to switch out. So if you nail the prediction and HP Grass the Pert, Dragon Claw the Gar, or Fire Blast the Skarmory, then you've just taken a huge chunk of HP off of one of the opponent's key physical walls. Staying on the topic of mixed attackers, we can include Mixed Tyranitar in the discussion: Max SpA / Max Speed (or near it) Tyranitar with a moveset of Ice Beam / HP Grass / Fire Blast / Brick Break threatens a HUGE portion of the metagame. It hits an excellent Speed tier, outspeeding all of its common switch ins, has virtually flawless coverage, being truly walled by only Gyarados, Milotic, and Suicune to an extent. Even these all are heavily neutered in their effectiveness by sand, making it easier for your teammates. All in all, it makes for a fantastic lead. Consider, for example, the situation against lead Skarmory. You can click Fire Blast risk free, knowing you should outspeed (if you don't outspeed, then you will OHKO their YoloSkarm). Lum Berry mitigates any risk of them clicking Toxic. So you nail the T1 Fire Blast, and now have an opportunity to hit everything on their team that switches in, barring one of the aforementioned Water types. Finally, Lum Berry additionally grants a huge momentum advantage against the sleep inducing leads like Venusaur and Jynx.
BKC Tar threatens every Mon in the game and gets up Sand immediately. It forces lead Electrics out, random lead Jirachi or Celebi, can fire off a Focus Punch against lead Skarmory (although this move is team dependent as sometimes you'd rather just switch out).
Zapdos should probably have been ranked higher since the two Hidden Power types appear individually, whereas they are ostensibly the same set. Zapdos makes for a great lead because it prevents early Spikes, and also poses the threat of SubPass in favorable lead matchups (Mence, Skarmory, Metagross). You can also make the cheeky move of just clicking T-Bolt against lead Tar into Dugtrio kill, if your team strategy is based around killing Tar and clearing its Sandstorm (think of a CM Cune or CurseLax style team). Additionally, in an offensive archetype, lead 328 Speed Zapdos can trade T-Wave with an opposing Zapdos. This is often an offensive team's best way of dealing with opposing offensive Zapdos.
Next up we have the three dangerous CB leads: Mence, Tar, and Metagross. CB Salamence makes a great lead because you're fast, reasonably bulky with Intimidate, and immediately force the opponent's Water Mon or Skarmory in against a lead Tyranitar. Almost always paired with Magneton, you can even try to pull off a double switch if you anticipate the opponent's Skarmory switch. CB Tar is a bit stronger than Mence by virtue of its STAB and access to Focus Punch. This makes it capable of OHKO'ing a huge portion of the metagame with the right move. And finally, CB Metagross is famous for its Explosion OHKO'ing Skarmory and having theoretically no good switch in to its Meteor Mash spam. If it Attack raises from the lead matchup against Tyranitar, things can get out of hand fast.
ADV OU Teambuilding/Strategy
Archetypes and Cores
A Data-Driven Approach
Table of Contents (linked)
- Goals, Data Sources, Methodology, Cores and Synergy, Archetypes and Confidence, Results, Set List, Lead Analysis
- Archetype Analysis: Milotic/Celebi Stall/Balance, Magneton Offense, Skarmbliss TSS, Spikeless Offense,
- Archetype Analysis (continued): Porygon2, Spikes Offense, Dugtrio Stall (/Balance), Spinner/Mag Balance/Bulky Offense
- Synergistic Cores Analysis, Antisynergistic Pairs Analysis
- Instructive Replays and Written Narrations
4/8/2020: Post-SPL XI updates: Added offensive Skarmbliss teams, Breloom into Pursuit Tar builds, beefed up Porygon2, WishBliss + Skarm as Claydol check, benefits of Taunt Skarmory in preventing healing, CBMence as stall breaker, and minor edits on Cloyster.
10/22/2019: Added comments on Starmie in Spikes Offense and weather reset/use of Dugtrio/Claydol in mono/double wincon teams
10/3/2019: Replays and narrations of Milo/Cel > Spikes Offense, Spikes Offense > Spikes Offense, and Spikes Offense > Skarmbliss TSS added.
9/28/2019: OP
10/22/2019: Added comments on Starmie in Spikes Offense and weather reset/use of Dugtrio/Claydol in mono/double wincon teams
10/3/2019: Replays and narrations of Milo/Cel > Spikes Offense, Spikes Offense > Spikes Offense, and Spikes Offense > Skarmbliss TSS added.
9/28/2019: OP
Introduction
Hi Everyone,
I recently wrote a program that does processing of sorts on teambuilders. Since then, I've been improving the program to slowly incorporate more varied statistics, to the point where I felt it could do a pretty comprehensive analysis on the metagame.
Goals
In this article, I am primarily interested in understanding team building in this metagame from a data-driven approach supported by experience, and sharing this knowledge. Fundamentally, I want to bridge the gap between a team dump that most players will be unwilling to share to keep their competitive advantage (and rightfully so), and the less experienced player who wants model teams to learn the metagame from. While team building guides such as this are useful for fitting pokemon into roles, they address little about their interactions.
Yet, understanding interactions is fundamental to good and creative teambuilding, not only from a position of advancing one's gameplan through the use of synergies but also defending against an opponent's gameplan. It is impossible to cover every mon that can be thrown out by one's opponent perfectly, so instead of covering every individual mon equally, many good creative teams focus on the big picture and find outs against opposing archetypes.
To this end, I will try to answer the questions: what are the broad archetypes that exist in the metagame? which pokemon and what sets go well together, and why? Which pokemon do not appear together, and why? In answering the latter question, I also hope to make a comparative analysis, meaning why choose one mon on a team over another?
To study these interactions, I wrote a program that takes a teambuilder, categorizes sets, measures the synergy across sets, and ranks them in order of importance. I have also made an attempt at categorizing these sets into Archetypes. In doing this analysis, I hope that the processed data can be a focal point for discussion, and in so become a teambuilding guide of its own. I have thus invited UD to contribute, scrutinize the results, and talk about any trends and interesting cores.
What This is Not
This is not an article for one to scout my sources. I take the confidentiality of my sources seriously, and I have combined and anonymized the results into groups. I have only released individual data if my source has consented to it. The focus will be on what and how cores synergize, and not on what these players prefer to use.
Data Sources
I have obtained generous team contributions from thelinearcurve, Astamatitos, McMeghan, Golden Sun, and UD, all of whom are notable players and builders in their own right. However, since these teams are not publicly accessible, a good reference for this discussion is thelinearcurve's public builder.
What is a Core, and what is an Archetype?
A core is a group of mons that function well together. In other words, they have synergy. An archetype is a group of teams that encompass a similar gameplan. Archetypes are frequently thought of as defined by a few key mons that are supplemented by a variety of cores that can be substituted with each other. Likewise, a core may belong to more than one archetype.
Methodology
The technical details of the work are in the spoilers below. Check them out if you want to know how the sets are categorized, how the synergies are measured, and how the archetypes are determined.
Categorization and Naming of Sets
Perhaps the most tricky thing about this project is the categorization of sets. There are more unique sets than teams in the builders to work with, so I ruled out using unsupervised clustering, meaning I have to actually think about how to split the sets instead of letting the computer do it. I have to thank Disaster Area and Zokuru for many ideas and discussions we've had on finding a good methodology.
The benchmark for a good categorization tool is Tyranitar. It has so many sets: Physical 4-attack, Choice Band, Dragon Dance, Pursuit, Mix Lead. Within DD variants, there are HP Grass or HP Bug fillers, and there are defensive and offensive EV spreads. A good methodology should distinguish all these sets from each other, and be able to name them appropriately.
I started with the item, and not just any item, but Choice Band (1). It is the single most easy-to-spot and accurate predictor of the set's function. Taking CBtar out of the equation, I noticed that all the sets above have unique EV priorities. Physical 4-attack is HP/Atk invested, DD is either Atk/Spe or HP/Spe, Pursuit is HP/SpA, Mix Lead is SpA/Spe. I thus created categories based on the top two EVs of the set (2).
To look for divisions within categories, I use the concept of synergy again -- not between mons, but between moves. If two moves occupy one slot, say HP Grass/HP Bug in the filler slot of DDtar, then we expect them not to appear together in the same set; in other words, the moves antisynergize (3). Of course, categories will be really cluttered if even the rarest sets were split this way (at this moment I do not treat it as a priority to account for the rare Substitute DDtar), so I only included moves above a probability and count threshold (4). I try to find pairs first that satisfy these conditions, if not then triplets. These four criterion labelled (1)-(4) determine the categories.
Naming categories is easy when they are split by moves or items, but what about those determined by EVs? I find that most of these sets are still tied to a unique move that mostly does not appear, or at most appears only once on the majority of the other sets. For example, Dragon Dance only exists on Atk/Spe or HP/Spe Tyranitar sets, so we could say that DD almost uniquely characterizes these sets.
The benchmark for a good categorization tool is Tyranitar. It has so many sets: Physical 4-attack, Choice Band, Dragon Dance, Pursuit, Mix Lead. Within DD variants, there are HP Grass or HP Bug fillers, and there are defensive and offensive EV spreads. A good methodology should distinguish all these sets from each other, and be able to name them appropriately.
I started with the item, and not just any item, but Choice Band (1). It is the single most easy-to-spot and accurate predictor of the set's function. Taking CBtar out of the equation, I noticed that all the sets above have unique EV priorities. Physical 4-attack is HP/Atk invested, DD is either Atk/Spe or HP/Spe, Pursuit is HP/SpA, Mix Lead is SpA/Spe. I thus created categories based on the top two EVs of the set (2).
To look for divisions within categories, I use the concept of synergy again -- not between mons, but between moves. If two moves occupy one slot, say HP Grass/HP Bug in the filler slot of DDtar, then we expect them not to appear together in the same set; in other words, the moves antisynergize (3). Of course, categories will be really cluttered if even the rarest sets were split this way (at this moment I do not treat it as a priority to account for the rare Substitute DDtar), so I only included moves above a probability and count threshold (4). I try to find pairs first that satisfy these conditions, if not then triplets. These four criterion labelled (1)-(4) determine the categories.
Naming categories is easy when they are split by moves or items, but what about those determined by EVs? I find that most of these sets are still tied to a unique move that mostly does not appear, or at most appears only once on the majority of the other sets. For example, Dragon Dance only exists on Atk/Spe or HP/Spe Tyranitar sets, so we could say that DD almost uniquely characterizes these sets.
It's not so much the frequency of the core, but how much more frequently the core appears together than if the constituents were just put together by chance. Official Smogon statistics already does this in calculating teammates. I'll take a slightly different approach based off the concept of Multivariate Pointwise Mutual Information (wiki on PMI, wiki on MMI, paper combining both) in natural language processing, that can be extended beyond pairs into triplet or quad cores. In short, this is a number that is positive when there is synergy (prefer to be teammates), negative when there is antisynergy (prefer not to be teammate), and zero when the mons appear independently.
Suppose I want to measure how synergistic the quintessential Magneton + Claydol core is. If Magneton and Claydol were independent, then the pair would appear roughly with probability equal to the product of individual probabilties, P(Magneton) * P(Claydol). A synergistic pair should exceed this probability, i.e. P(Magneton, Claydol) > P(Magneton) * P(Claydol). An antisynergistic pair, like Forretress and Skarmory, where you would tend to only use one as your spiker, should give a lower than expected probability, i.e. P(Forretress, Skarmory) < P(Forretress) * P(Skarmory). It thus makes sense for the synergy of a pokemon pair (X, Y) to by defined by P(X, Y) / [P(X) * P(Y)], which means the number of times over just pure chance that this pair to appear together. This number is 1 when the pair is independent. To get a number that has more sensible properties like being 0 when independent and (negative) positive when (anti) synergetic, we use the logarithm to obtain the final synergy score = log2{P(X, Y) / [P(X) * P(Y)]}. Similar formulas can be derived for triplet cores, that compare the core probability with constituent pairs and individual mons.
For those who don't want to understand the math, a score of +1/+2/+3 means 2/4/8 times more frequent than expected from combining individual (and lower order) probabilities. Similarly a score of -1/-2/-3 means 2/4/8 times less frequent than expected.
Suppose I want to measure how synergistic the quintessential Magneton + Claydol core is. If Magneton and Claydol were independent, then the pair would appear roughly with probability equal to the product of individual probabilties, P(Magneton) * P(Claydol). A synergistic pair should exceed this probability, i.e. P(Magneton, Claydol) > P(Magneton) * P(Claydol). An antisynergistic pair, like Forretress and Skarmory, where you would tend to only use one as your spiker, should give a lower than expected probability, i.e. P(Forretress, Skarmory) < P(Forretress) * P(Skarmory). It thus makes sense for the synergy of a pokemon pair (X, Y) to by defined by P(X, Y) / [P(X) * P(Y)], which means the number of times over just pure chance that this pair to appear together. This number is 1 when the pair is independent. To get a number that has more sensible properties like being 0 when independent and (negative) positive when (anti) synergetic, we use the logarithm to obtain the final synergy score = log2{P(X, Y) / [P(X) * P(Y)]}. Similar formulas can be derived for triplet cores, that compare the core probability with constituent pairs and individual mons.
For those who don't want to understand the math, a score of +1/+2/+3 means 2/4/8 times more frequent than expected from combining individual (and lower order) probabilities. Similarly a score of -1/-2/-3 means 2/4/8 times less frequent than expected.
This is clearly a clustering problem, and the defining feature of this problem is that mons can appear in more than one archetype. Therefore, it makes sense to provide a score that determines the confidence that each mon is in each archetype, so this is the realm of fuzzy clustering algorithms. If we now imagine every set as a node on a network, with the strength of linkages determined by their usage frequency, then the process of finding clusters is intuitively understood: How can we cut the network into some number of completely separate partitions while making sure that we've on average cut only weak links? It turns out that an algorithm called spectral clustering does just that.
I formed an adjacency matrix representing a graph where nodes are the mon sets, and the weights on the edges are the probability that the two nodes co-appear. Then, I used normalized spectral clustering described by Ng, Jordan and Weiss (2002). For the clustering step, I used fuzzy C-means clustering instead of the usual K-means. I determined the number of clusters by finding the point of a sharp fall-off in a plot of the fuzzy partition coefficient vs number of clusters, and tuning the exponent so that the clusters in the data visually had high representation and tightness.
I formed an adjacency matrix representing a graph where nodes are the mon sets, and the weights on the edges are the probability that the two nodes co-appear. Then, I used normalized spectral clustering described by Ng, Jordan and Weiss (2002). For the clustering step, I used fuzzy C-means clustering instead of the usual K-means. I determined the number of clusters by finding the point of a sharp fall-off in a plot of the fuzzy partition coefficient vs number of clusters, and tuning the exponent so that the clusters in the data visually had high representation and tightness.
Core Rankings - Synergy and Frequency
Cores can either be strongly or weakly synergistic (given by the synergy score), and they can be frequent or infrequent (given by the frequency label). The final rankings that you see in the files labelled “_synergy_sets_statistics” are a combination of both – they are determined by a weighted product with an exponent. Weighting synergy too much may cause very specific cores of BL mons to appear at the top e.g. Sun teams, while, weighting frequency gives us not much more information than single mon frequencies.
Archetypes - Confidence and Frequency
The files labelled “_archetype_statistics” are the results of an attempt to categorize synergies into broad archetypes. These archetypes are completely blind to human input, meaning I did not ask for specific criteria to be met such as requiring Tyranitar in TSS. Each archetype is labelled with a number and is tagged to a ranked list, and the rankings are again determined by two factors. This time, one of the indicators is the level of confidence that the set is in the archetype. This is a number from 0 to 1, and can be thought of in percentages where the higher the number, the more likely this mon is in the archetype. The other indicator is frequency in the entire builder (not in the archetype!). Just as I did with the cores, the final rankings are a combination of both, a weighted product. Those at the top are most likely to be in the core.
Results
I roughly sorted the teams I received into two groups, based on my understanding of the contributors' team timelines, building style, and the current state of the metagame. This allows each document to be less cluttered, so that the cores become immediately obvious. The analysis though, will be focused on the group that I feel is more current.
Four main files are produced for each group, and all data is gathered into a zip file attached called "ADV_OU_Archetypes_Cores_Data_vapicuno.zip". The first three are available in .txt, .csv and sometimes .xlsx. csv files can be open in Microsoft Excel.
1. Legend indicating set categories – “statistics_legend”
2. Set core synergy statistics (includes lead statistics) -- “synergy_sets_statistics”
3. Archetypes -- “archetype_statistics”
4. Pokemon core synergy statistics, a cruder version of (1) – “synergy_statistics”
For the group that we studied, I have also included a file of set pairs that do not appear together -- “nonappearances”. thelinearcurve has also given me the liberty of compiling an aggregated set list from his teams, so that is also available in four formats -- the full set list, and filtered lists removing 1/8, 1/16 and 1/32 of the least relevant sets. You can see for example the 1/16 set list online by clicking here, where sets are aggregated and compiled into slashed moves through the program, for example like this:
Lead Analysis
This is primarily a UD contribution, thank him!
Code:
Team Lead Arranged by Frequency
Counts | Freq (%) | Lead
19 | 7.884 | HP/SpA Crunch Tyranitar
13 | 5.394 | HP/Atk FP Earthquake Tyranitar
13 | 5.394 | HP/SpD Whirlwind Skarmory
13 | 5.394 | SpA/Spe FB Salamence
12 | 4.979 | SpA/Spe HP Ice Thunderbolt Zapdos
11 | 4.564 | Band Salamence
11 | 4.564 | SpA/Spe HP Grass Thunderbolt Zapdos
11 | 4.564 | Band Metagross
10 | 4.149 | Band Tyranitar
10 | 4.149 | HP/SpD Roar Skarmory
8 | 3.320 | SpA/Spe BB TyranitarTogether, these 11 leads make up >54% of the total lead Mon appearances, or in other words, would be considered the most common leads in the game. This more or less jives with conventional ADV wisdom. These top leads have much in common – Most (except Zapdos) have early game prowess either with Spikes or heavy chip damage, and in particular Salamence/Tyranitar have great versatility, forcing opponents to spend a turn on the back seat just scouting its set.
Pursuit Tar makes a great lead because it forces out opposing lead Zapdos, can hit Skarmory extremely hard with Fire Blast, sets up Sand from the get go. Occasionally, you will find yourself matched up with an opposing lead Gengar. Typically that indicates a Hypnosis + Explosion set. Either way, they must risk hitting a 60% move and / or losing the prediction game of Pursuit vs. Crunch. In other words, it's an extremely advantageous lead scenario. Pursuit Tar also typically has one coverage move to work with (Pursuit, Crunch, and a Fire move are the presumptive first three slots), so you can threaten opposing Pert lead (rare in itself) with HP Grass, or cheeky Zapdos staying in with Rock Slide. Ice Beam is an option as well, though that move presents zero upside in a T1 situation as you must switch from opposing lead Flygon or Mence, fearing their OHKO move.
Skarmory comes in at number two to no surprise. It lays a Spike against every lead other than Zapdos. However, it risks taking upwards of 70% from opposing Tar and Mence Fire Blast. But some teams would rather get that Spike on T1 anyway, making the risk worth the reward. We will ignore the next appearance of Skarmory (Roar vs. Whirlwind usually means Drill Peck, but it could also just be a bluff or picking the wrong phazing move by accident)
Tied for third are regular four attacks Leftovers Tar (BKC Tar) and MixMence. Both make for fantastic leads. MixMence can score a huge T1 advantage if faced off against opposing Tar, who is more or less obligated to switch out. So if you nail the prediction and HP Grass the Pert, Dragon Claw the Gar, or Fire Blast the Skarmory, then you've just taken a huge chunk of HP off of one of the opponent's key physical walls. Staying on the topic of mixed attackers, we can include Mixed Tyranitar in the discussion: Max SpA / Max Speed (or near it) Tyranitar with a moveset of Ice Beam / HP Grass / Fire Blast / Brick Break threatens a HUGE portion of the metagame. It hits an excellent Speed tier, outspeeding all of its common switch ins, has virtually flawless coverage, being truly walled by only Gyarados, Milotic, and Suicune to an extent. Even these all are heavily neutered in their effectiveness by sand, making it easier for your teammates. All in all, it makes for a fantastic lead. Consider, for example, the situation against lead Skarmory. You can click Fire Blast risk free, knowing you should outspeed (if you don't outspeed, then you will OHKO their YoloSkarm). Lum Berry mitigates any risk of them clicking Toxic. So you nail the T1 Fire Blast, and now have an opportunity to hit everything on their team that switches in, barring one of the aforementioned Water types. Finally, Lum Berry additionally grants a huge momentum advantage against the sleep inducing leads like Venusaur and Jynx.
BKC Tar threatens every Mon in the game and gets up Sand immediately. It forces lead Electrics out, random lead Jirachi or Celebi, can fire off a Focus Punch against lead Skarmory (although this move is team dependent as sometimes you'd rather just switch out).
Zapdos should probably have been ranked higher since the two Hidden Power types appear individually, whereas they are ostensibly the same set. Zapdos makes for a great lead because it prevents early Spikes, and also poses the threat of SubPass in favorable lead matchups (Mence, Skarmory, Metagross). You can also make the cheeky move of just clicking T-Bolt against lead Tar into Dugtrio kill, if your team strategy is based around killing Tar and clearing its Sandstorm (think of a CM Cune or CurseLax style team). Additionally, in an offensive archetype, lead 328 Speed Zapdos can trade T-Wave with an opposing Zapdos. This is often an offensive team's best way of dealing with opposing offensive Zapdos.
Next up we have the three dangerous CB leads: Mence, Tar, and Metagross. CB Salamence makes a great lead because you're fast, reasonably bulky with Intimidate, and immediately force the opponent's Water Mon or Skarmory in against a lead Tyranitar. Almost always paired with Magneton, you can even try to pull off a double switch if you anticipate the opponent's Skarmory switch. CB Tar is a bit stronger than Mence by virtue of its STAB and access to Focus Punch. This makes it capable of OHKO'ing a huge portion of the metagame with the right move. And finally, CB Metagross is famous for its Explosion OHKO'ing Skarmory and having theoretically no good switch in to its Meteor Mash spam. If it Attack raises from the lead matchup against Tyranitar, things can get out of hand fast.
Last edited:
