Former WWE Interviewer To Make AEW In-Ring Debut This Week
August 10, 2020No one saw it coming but one AEW talent is actually gonna wrestle for the company.
Since AEW launched they have had Dasha Gonzalez, the former Dasha Fuentes In WWE, in the company as a backstage interviewer and ring announcer. She has also been used for Spanish commentary as well. With Dasha having so many different roles in the company no one expected her to add more to that. Especially adding in-ring performer to that list of duties.
Today AEW announced that Dasha will be competing in the AEW Women’s Tag Team tournament on tonight’s episode. When picking what team she will be on Dasha pulled a blue chip. Former NXT wrestler Rachael Ellering then walked up with a blue chip confirming that the two will be teammates in the tournament. It was then announced that Gonzalez and Ellering will be taking on the team of Ivelisse and Diamante tonight.
As far as Dasha and her history of wrestling in a ring. The only match she has on record is a six-women tag team match at an NXT live event back in 2015. She lost to Alexa Bliss, Carmella, and Devin Taylor teaming with Becky Lynch and Dana Brooke.
The AEW Women’s Tag Team Tournament is posted Monday night’s at 7pm ET/4pm PT on the AEW YouTube Channel.
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冰火两重天 可亲胸亲私密处 艾奥瓦州约炮楼凤 delves into the technical aspects of this.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
Spot on about the indexing delays. It’s not just about building the link anymore; it’s about the “stickiness” of the placement. We’ve been focusing heavily on that metric lately.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
I bookmarked this for my team. The section on avoiding footprints is crucial. We recently audited a site that got hit exactly because they ignored that principle. Good catch.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
Finally, someone said it. The old school “blast and pray” method is dead. Precision and camouflage are the new standard.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
I bookmarked this for my team. The section on avoiding footprints is crucial. We recently audited a site that got hit exactly because they ignored that principle. Good catch.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
I’d argue that the content relevance is even more critical now. We’ve seen perfectly good links get devalued just because the semantic match wasn’t tight enough.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
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Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
I’m sharing this with our content team. We’ve been struggling to explain why “quality over quantity” isn’t just a cliché, and this illustrates it perfectly.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
I bookmarked this for my team. The section on avoiding footprints is crucial. We recently audited a site that got hit exactly because they ignored that principle. Good catch.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
Spot on about the indexing delays. It’s not just about building the link anymore; it’s about the “stickiness” of the placement. We’ve been focusing heavily on that metric lately.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
This is a solid breakdown. One thing I’d add is that the impact of these updates often lags by 2-3 weeks. We tracked this across multiple projects and found the recovery phase is where most people give up too early.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between “diversity” and “randomness” is what saves you during a Core Update.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.