I analyzed all writers on Game Theory
Credibility score: 49/100 — Mixed Credibility. Several questionable claims detected. Watch with healthy skepticism.
BSmeter analyzed "I analyzed all writers on Game Theory" and rated it 49/100 for credibility (a BS score of 51/100 — mixed credibility), on 2026-08-09. Its weakest claim — "'Ules K formula' — drops an obscure metric with zero context" — scored 20/100 and was flagged as anonymous authority. 41 claims were checked against the video transcript. Scores are produced by BSmeter's AI analysis of the transcript, not independent human verification.
Of 41 claims analyzed: 6 scored under 40, 28 between 40 and 69, and 7 at 70 or above.
Claims analyzed
'I downloaded all the transcripts' — absolute language with zero receipts — Missing Context (45/100)
At 0:00
Says 'all' like it's a done deal — never shows the actual download list or scope
Why this score: The speaker uses the word 'all' to signal exhaustive coverage, but then immediately admits a major methodological oversight. This creates a confidence mismatch: the claim sounds comprehensive while the evidence offered is thin. A neutral framing would say 'I downloaded transcripts from a large sample' or specify how many videos were actually included.
Original quote: “Is game theory more popular when it's positive or negative? Being positive is the best? Well, a lot of advice on YouTube says being negative performs better. Oh, in a previous video, I downloaded all the transcripts from the YouTube channel Game Theory in order to analyze their words. I was…”
Dismisses reading descriptions as obvious, ignores viewer suggestion — Loaded Language (45/100)
At 0:30
Frames reading descriptions as weird — turns a simple tip into something only weirdos would do.
Why this score: The speaker mocks the practical suggestion by implying it's obvious yet somehow beneath notice. This is the classic 'I knew that' framing that dismisses audience input without engaging it. A neutral version would be 'good catch, I should have checked the credits.'
Original quote: “You all pointed out that I could just look at the description of each video to see who wrote it. Who even reads the description? I even got a comment from Game Theory informing me of this.”
Sets up binary outcome: meaningful analysis or total waste — False Dilemma (35/100)
At 1:46
Only two options offered — either the data works perfectly or it's worthless. Ignores partial or useful results.
Why this score: This is textbook false dilemma framing. Real data analysis often produces messy but still informative results — some writers might be separable, some might cluster, some patterns might emerge even with overlap. By framing it as 'meaningful or waste,' the speaker creates artificial stakes that make the project seem more dramatic than it is.
Original quote: “The biggest issue with using these writing credits is that for many of these episodes, there are multiple writers credited. So, I'll have to see if I can effectively separate out the writers in order to produce a meaningful analysis. Otherwise, this is just a big waste of time.”
Promises analysis of 'all writers' across four channels — scope inflation framing — Missing Context (45/100)
At 2:30
Says 'all writers' then limits to 50+ with 60-script cutoff — still calls it comprehensive.
Why this score: The framing sets up an exhaustive study, but the actual scope is narrowed to a small subset without acknowledging how many voices get filtered out. A neutral version would say 'the most prolific writers' rather than 'all writers.'
Original quote: “four theorist channels. We'll be revealing individual writing patterns, comparing writers to one another, and performing sentiment analysis on them. And finally, we'll try to answer the question, do videos with more negativity get more views?”
Co-authorship labeled 'biggest issue' — framing choice that sets up later workaround — Loaded Language (45/100)
At 4:44
Calling it the 'biggest issue' primes viewers to see co-authorship as a problem rather than just a data feature.
Why this score: By labeling co-authorship as the main obstacle, the framing steers the audience toward accepting whatever workaround follows as a clever fix. A neutral version would simply state 'co-authorship creates attribution complexity' without the loaded 'biggest issue' phrasing.
Original quote: “The biggest issue with doing this type of analysis is the co-authorship.”
'Thankfully' + 'I think I have enough' — confidence mismatch between certainty and actual evidence — Confidence Mismatch (35/100)
At 5:27
Opens with 'thankfully' then hedges with 'I think' — the reassurance and uncertainty are in direct conflict.
Why this score: The word 'thankfully' implies a lucky break that saves the analysis, yet 'I think' immediately undercuts any certainty about whether those combinations are actually sufficient. This creates a confidence mismatch: the framing wants you to feel relief, but the language reveals doubt.
Original quote: “Thankfully most of the writers tend to write with different writing partners for each video. So, I think I have enough different combinations of writers to create a meaningful analysis for each one.”
'By far larger' — volume game that emphasizes scale without context of total output — Volume Game (45/100)
At 5:41
The phrase 'by far larger' spotlights the raw number while skipping what share 180 episodes represents of Matt's total work.
Why this score: By focusing on the absolute count (180) and the comparative language ('by far larger'), the framing emphasizes dominance without revealing whether this represents 30% or 80% of Matt's episodes. A neutral version would give the proportion or total baseline for context.
Original quote: “For Matt, uh, you and he wrote like 180 episodes together, which is by far larger than anyone else.”
'all four channels' — assumes total coverage without caveats — Missing Context (45/100)
At 6:39
Frames the dataset as complete — but immediately admits older episodes lack transcripts.
Why this score: By leading with 'all four channels' the speaker sets up an impression of exhaustive data, then quietly qualifies it two sentences later. A neutral frame would say 'all available transcripts' up front so listeners know the scope from the jump.
Original quote: “in order to analyze each writer, we first need to download all of the transcripts for all four channels”
'pretty terrible' auto-transcripts — dismisses the workaround — No Frame (75/100)
At 6:52
Straight talk: names the actual limitation and its consequence without spin.
Why this score: They acknowledge both the historical gap and the poor quality of auto-generated text, then explain why they exclude those episodes. Clean framing because the limitation is stated plainly rather than hidden behind reassuring language.
Original quote: “they didn't used to upload transcripts back in the day. Doesn't YouTube au transcribe those episodes? Yes. And it is pretty terrible.”
Ranking writers by sentence length — treats the metric as meaningful — Missing Context (45/100)
At 7:16
Presents sentence length as a stylistic fingerprint while skipping why it matters.
Why this score: The speaker never explains what longer sentences reveal about writing quality or audience engagement. A neutral version would note that sentence length is one narrow proxy among many and may simply reflect episode format rather than individual style.
Original quote: “If we look at which writers use the longest sentences, we see Zach has the highest number of words per sentence, followed by Matt and Tom.”
'Ules K formula' — drops an obscure metric with zero context — Anonymous Authority (20/100)
At 8:22
Cites a formula no viewer can verify or understand from the name alone.
Why this score: By invoking 'Ules K' without defining it or linking to a source, the speaker borrows the authority of a technical term while leaving the audience unable to assess whether the metric fits the claim. A transparent frame would either explain the formula in one sentence or link to it in the description.
Original quote: “I'm using the Ules K formula, which is a bit counterintuitive as Tom learned.”
Frames 'vocabulary richness' as repetition metric — inverts normal meaning — Loaded Language (45/100)
At 8:34
Calls higher score 'more repetitive' when audience expects 'richer' — flips intuition
Why this score: By redefining richness as repetition, they make the metric feel backwards before explaining why. Neutral framing would say 'this measures repetition, not richness' instead of letting the word 'richness' do the confusing work.
Original quote: “just to be clear. So um this is vocabulary richness higher is more repetitive in for this one here.”
Presents TTR as 'simplest' way despite known bias against long texts — missing context — Missing Context (45/100)
At 8:42
Calls TTR simplest while skipping its fatal flaw: longer texts automatically score lower
Why this score: Omitting the length bias makes TTR seem neutral. Fair framing would mention 'this method penalizes writers with more output' so viewers understand why they're switching to Yules K.
Original quote: “and this is more diverse. So the simplest way to calculate vocabulary richness is by using type token ratio.”
Claims personal observation as general rule — anecdotal authority — Anonymous Authority (45/100)
At 9:15
'In my experience' presented as universal pattern with zero sample size or data shown
Why this score: Personal observation gets elevated to rule without evidence. Clean framing would say 'in the texts I tested' or give actual numbers instead of implying broad validity.
Original quote: “In my experience, whoever writes the most has the lowest score, and whoever writes the least has the highest score.”
Positions Yules K as upgrade without showing why it's better — confidence mismatch — Confidence Mismatch (45/100)
At 9:21
Calls it 'more sophisticated' while audience still doesn't know what problem it solves
Why this score: Sophistication claim assumes listeners accept complexity as improvement. Better framing would explain the specific bias it corrects before declaring superiority.
Original quote: “So, instead, I prefer to use ules K, which is a more sophisticated formula.”
Interprets higher Yules K as 'saying a lot of the same thing' — oversimplifies repetition — Missing Context (45/100)
At 9:56
Reduces repetition score to 'same thing' without clarifying if it's topic, phrasing, or actual redundancy
Why this score: Repetition could mean stylistic consistency or lazy writing. Neutral framing would distinguish between intentional reuse versus filler before labeling Forest repetitive.
Original quote: “the higher is more repetitive. So it's saying Forest is saying a lot of the same thing.”
Frames complex vocabulary as problem needing 'dumb down' — loaded choice — Loaded Language (45/100)
At 10:07
'Dumb this down' implies audience can't handle advanced words rather than 'adjust register'
Why this score: The phrase positions vocabulary as obstacle instead of asset. Alternative framing: 'match audience reading level' keeps respect for both writer skill and viewer intelligence.
Original quote: “Yeah. He one of the notes he used to get very early on is, "Hey, you need to dumb this down because he has such a good vocabulary"”
Names 'Fletch Concaid formula' — no source, sounds official — Anonymous Authority (45/100)
At 10:36
'Fletch Concaid' drops like a real metric — zero attribution, classic authority trick.
Why this score: Calling it 'the' formula implies institutional backing that never materializes. Without naming origin, method, or validation, the claim rides on the listener accepting the label at face value instead of asking where it came from.
Original quote: “Speaking of grade level let's take a look at the reading level for each writer for this analysis. I'm using the Fletch Concaid grade level formula. What it's actually a surprisingly simple formula.”
Simplifies Flesch-Kincaid into 'big words = high score' — loses the actual formula — Missing Context (55/100)
At 10:46
Reduces a multi-variable formula to one intuitive rule — easy to follow, technically incomplete.
Why this score: The real Flesch-Kincaid uses syllables per word and words per sentence, not 'big words.' By boiling it down to a single visual heuristic, the speaker trades precision for accessibility, making the later rankings feel more obvious than the math actually supports.
Original quote: “So this is just taking word length and sentence length and incorporating that into the formula. So, someone who uses a lot of big words and a lot of long sentences, they get a high score.”
Cites previous video as fact-check for current claim — circular sourcing — Anonymous Authority (40/100)
At 10:58
'The AI analysis noticed' — unnamed model, unnamed data, treated as external proof.
Why this score: By outsourcing the observation to 'the AI,' the speaker dodges responsibility for the finding while still using it to validate their own earlier statement. Without the model, training data, or prompt disclosed, the authority is both convenient and unverifiable.
Original quote: “Didn't you talk about that in the last game theory video? Yes. The AI analysis noticed that Matt asks more questions of the viewer than Tom does.”
Deflects responsibility for Tom's top ranking — passive voice dodge — Loaded Language (45/100)
At 12:42
"These are not my choices" — distancing language to avoid owning the ranking.
Why this score: By framing the phrase selection as something the system "told" him to look for, the speaker creates a buffer between himself and the surprising result. The passive construction "I don't take any responsibility" is a rhetorical move that lets him present the data while dodging accountability for the method that produced it. A neutral framing would acknowledge that he chose both the system parameters and the interpretation, rather than positioning himself as a passive recipient of algorithmic outputs.
Original quote: “"But that feels a little weird, right?" >> "But what does it really mean? How did you choose these phrases? These are not my choices. These are what I was told to look for. So, I don't take any responsibility for the fact that Tom is actually at the top of this list."”
Bug fix quietly moves Tom from top to middle — transparency without accountability — No Frame (75/100)
At 13:44
Straightforward admission of error and correction — no deflection, just data.
Why this score: This is clean framing. The speaker explicitly names the bug (dropping instances of "right" due to question mark handling), shows the before-and-after effect on Tom's ranking, and presents the corrected result without trying to spin it. A neutral framing would include this level of methodological transparency, and that's exactly what happens here. The contrast with the earlier passive-voice distancing is instructive — when the data moves in an expected direction after correction, he owns the process completely.
Original quote: “"Yes. I went down a rabbit hole trying to understand exactly what might be causing these counterintuitive results. It looks like I had a bug with my reject and I was dropping instances of right because of the question mark. When I fixed it and added it back in, Tom's rhetorical rate increased, but…”
'Deciding what counts' as rhetorical phrases alters results — Missing Context (45/100)
At 14:32
Frames arbitrary list choices as neutral methodology — omits how the cutoffs were actually set.
Why this score: By saying 'just deciding' they downplay the subjective step as routine, yet this single choice cascades through every ranking they show. Without the explicit inclusion/exclusion rules, viewers can't judge whether the 'large impact' is an artifact of cherry-picked definitions or a robust finding.
Original quote: “just deciding what we consider to be in the list of rhetorical phrases can have a large impact on the results”
'Top of the list' is Luke, Justin, and Tom — framing their own names as objective data — Confidence Mismatch (35/100)
At 15:23
Lists themselves first without noting the obvious self-reference bias.
Why this score: The ranking is presented as a neutral output, yet two of the top three names belong to the people conducting the analysis. The framing treats the result as surprising ('really interesting') rather than the predictable outcome of self-inclusion, which quietly inflates perceived credibility.
Original quote: “If we rank the writers by who uses these devices the most, we see at the top of this list is Luke, Justin, and Tom”
'Bad habit I drilled out of others' — self-appointed writing authority — Anonymous Authority (40/100)
At 15:33
Claims to have trained 'all the other writers' with zero evidence beyond anecdote.
Why this score: The phrasing positions one person's stylistic preference as an objective improvement that was then imposed on colleagues. Without corroboration from those writers or independent metrics, the 'drill out' claim functions as unverified authority rather than documented process.
Original quote: “the reason that phrase started popping up for me in scripts was because of another bad writing habit I used to have that I then tried to drill out of all the other writers as well”
Singular pronouns: Amy tops, Tom half — loaded ranking framing — Loaded Language (45/100)
At 16:41
Ranks Amy highest, Tom half — implies quality difference without context
Why this score: By framing pronoun count as a direct comparison, the video suggests Amy's higher usage is superior while ignoring channel style differences. Neutral framing would note both counts without hierarchy.
Original quote: “Amy is at the top, whereas Tom only uses them about half as often.”
Plural pronouns: Amy #1, Tom #2 — ranking implies value — Loaded Language (45/100)
At 16:52
Same ranking move with plural pronouns — still no explanation why higher is better
Why this score: The segment keeps the same 'top = better' framing even though plural 'we' usage serves different rhetorical purposes. Without clarifying what the metric measures, the ranking stays decorative rather than analytical.
Original quote: “Amy is still at the top, but Tom has also jumped up to second place.”
We vs I shift explained as deliberate branding choice — No Frame (75/100)
At 17:05
Names the actual reason for pronoun change — transparent about the choice
Why this score: The speaker directly explains the move away from 'Matt did X' to 'we did X' as a conscious branding decision after Matt's departure. This is straightforward framing disclosure rather than hidden persuasion.
Original quote: “It is a part of the history. He always will be. We're in a new phase and he's no longer with the company. And so, there was just an effort to try and maybe be a little bit less overt.”
I-statements risk audience pushback — audience awareness framing — No Frame (75/100)
At 17:45
Acknowledges audience perception as real constraint on pronoun choice
Why this score: Speaker recognizes that viewers notice when multiple writers exist yet the host says 'I' constantly. This is clean framing — it states the social dynamic without pretending the choice is purely stylistic.
Original quote: “Using a lot of I statements can sometimes rub people the wrong way cuz they're like, well, we can see there's another writer on this.”
Pronoun difference tied to channel age and content style — No Frame (75/100)
At 17:53
Explains why channels differ — age + format, not just personal preference
Why this score: Speaker connects pronoun patterns to concrete factors: newer channels need more first-person storytelling, while older objective channels favor 'we.' This framing grounds the data in context instead of leaving rankings floating.
Original quote: “Style and food are different. Well, one style was very young when Amy took over... So it was a lot of I but both food and style do a lot more experiments... whereas film and game are much more objective”
Framing as aggressive vs passive language choice — No Frame (75/100)
At 18:30
Shows how "I" vs "you" statements soften tone — neutral example, no spin.
Why this score: Speaker demonstrates framing technique with concrete examples rather than asserting a loaded claim. The comparison between "You are not making sense" and "I'm not sure I understand" is a classic illustration of how pronoun choice shifts perceived aggression. This is straightforward linguistic explanation without manipulation.
Original quote: “there's another consideration that I think might also be important to look at, which is just how you frame things. For example, if I'm an aggressive person, I might say, "You are not making sense." But if I'm a more passive person, I'll say, "I'm not sure I understand what you're saying."”
Stylometric analysis claims similarity patterns based on pronoun use — Missing Context (45/100)
At 18:58
Presents heatmap results as objective similarity scores — omits how function-word patterns might reflect channel style more than personal voice.
Why this score: The speaker asserts that function words like "the" and "of" are "less prone to manipulation" and thus reliable for detecting genuine stylistic similarity. However, the analysis compares writers across different channels (Game Theory vs Food Theory scripts) where house style may override individual voice. The claim that "you haven't written that many scripts with him compared to Matt" is used to frame the similarity result as surprising, but this assumes equal opportunity for stylistic convergence regardless of content constraints. Without controlling for channel-specific writing guidelines,…
Original quote: “Next, I want to compare the writers to each other using styometric analysis. What? That's where we analyze different features of the writers in order to see which writers are most similar. ... The results of this analysis provide a similarity score between every combination of writers. I took these…”
Assumes family ties explain writing similarity — correlation framed as causation — Missing Context (45/100)
At 20:30
Treats blood relation as the main driver while ignoring shared editing and style guides.
Why this score: By saying 'being related that's going to happen,' the speaker frames family as the primary cause of stylistic overlap. The data only shows correlation; it doesn't rule out common templates, tone meetings, or house style that would exist even without the family link. The missing context is how much of the similarity survives once those shared processes are controlled for.
Original quote: “those will look different because he's trying to work within mine and Sant's different writing styles. So there's some element of that Eddie and I and and his scripts don't often take much polishing because he writes in a similar way to how I write and how I think which yeah being related that's…”
Blames horror content for low positivity — content type framed as sole driver — Missing Context (45/100)
At 21:17
Attributes sentiment scores only to topic choice, leaving out editing filters and channel persona.
Why this score: The speaker presents the horror channel's negativity as an inevitable result of its subject matter, but that ignores the editorial layer: the same games could be narrated with wonder, curiosity, or even dark humor that doesn't tank positivity scores. Meanwhile, Amy and Santi's higher scores are credited to 'helping the viewer,' yet the same helping frame could be applied to horror videos that teach game design or storytelling craft. The missing context is whether the sentiment gap persists once you normalize for editorial tone rather than just topic.
Original quote: “Now, yeah, 100%. Now, let's look at the sentiment analysis of the writers. Sentiment analysis, that's where we have the computer try to analyze the context of the words written by each writer to see how positive and negative they are. Amy is the most positive, while at the bottom, Zach, along with…”
Positivity always equals low negativity — assumes perfect inverse — Missing Context (45/100)
At 22:37
States inverse relationship as default — ignores possibility of neutral tone.
Why this score: By framing negativity as the automatic flip side of positivity, they sidestep the reality that writers could simply be neutral. The missing context is that sentiment isn't a zero-sum scale.
Original quote: “Writers with high positivity had low negativity. Though that doesn't have to be the case.”
Negativity always fails long-term — moral + market argument — Emotional Button (35/100)
At 22:49
Blends ethical stance ('don't punch down') with business outcome ('fizzle out') — double justification.
Why this score: They equate moral superiority with market survival, implying channels that stay negative die. This emotional button pushes viewers to accept positivity as both the right and smart choice, skipping data on whether rage-bait channels actually collapse or just rebrand.
Original quote: “Yeah, we always want to strive for positivity more. So again, like we don't want to punch down on people. And also if you're just constantly being negative, it gets draining. It gets tiring. Rage bait channels and like drama channels exist and they all sort of fizzle out eventually because just…”
Horizontal trend lines prove no sentiment-view correlation — visual proof — Missing Context (55/100)
At 23:17
Calls lines 'almost horizontal' without showing variance or R² — visual confidence without numbers.
Why this score: A horizontal line on a scatter plot only says the slope is near zero; it doesn't tell us how scattered the points are. Without the actual correlation coefficient or confidence interval, viewers can't judge how 'not huge' the relationship really is.
Original quote: “Finally, I want to compare episode sentiment to views to see what gets you more views. Exactly. Does being more negative get you more views? ... these lines are almost horizontal. Meaning you could be positive and get views or not. You could be negative and get views or not. There's not a huge…”
Thumbnails beat tone for clicks — quick pivot to marketing — No Frame (75/100)
At 24:10
Straight pivot from sentiment to thumbnail effect — acknowledges limits without overclaiming.
Why this score: They correctly narrow the claim: negativity itself isn't the driver, but packaging is. This is clean framing because they explicitly limit the conclusion to what their data can support.
Original quote: “No, I would say whoever says, 'Hey, be more negative, you get more views.' Not for not for the theorists. Yeah. I know what we found is for things like title and thumbnail can sometimes help.”
Positive vs negative framing as binary choice — false dilemma — False Dilemma (45/100)
At 24:47
Reduces complex tone decisions to two poles, ignoring nuance.
Why this score: Classic false dilemma framing. By presenting only 'positive' vs 'negative' as the available options, they're omitting the spectrum of tones (analytical, skeptical, humorous, etc.) that creators actually use. This framing makes the choice seem more binary than it is.
Original quote: “So, it's like, are you going to be positive? Are you going to be negative? You will probably get stuck in that way for a good while.”
Positive framing is a permanent branding trap — missing the choice to pivot later — Missing Context (45/100)
At 24:54
Frames channel tone as a permanent lock-in — omits creators who deliberately rebrand.
Why this score: By saying 'you'll probably get stuck,' the speaker sets up a false inevitability. A neutral framing would acknowledge that channels do pivot tone over time, even if it's harder. The loaded choice is between 'start positive, stay positive' vs. 'start negative, get stuck,' leaving out the possibility of strategic tone shifts.
Original quote: “So, it's like, are you going to be positive? Are you going to be negative? You will probably get stuck in that way for a good while. It will take a long time to move out of that realm which is why we prefer to skew positive if we can because one the internet can be a bit of a treacherous place…”
Negative channels burn out fast — positive ones last, 'especially as it makes no difference' — False Equivalence (35/100)
At 25:19
Equates 'drama channels die out' with 'positive = always better' — ignores successful long-running negative shows.
Why this score: The speaker uses 'makes no difference' to flatten two different strategies into one moral choice. A fair framing would compare retention data across both tones instead of asserting positivity wins by default. The rhetorical move is turning an aesthetic preference into a strategic inevitability.
Original quote: “whereas I think a lot of the negative channels can kind of have these big blow up bursts when drama happens or when something happens like oh yeah we can really hammer on this but over time people get bored so it's always better to start at the positive end if you can, especially as it makes no…”
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